Consumption of iodized salt (% of households)

Source: worldbank.org, 03.09.2025

Year: 2019

Flag Country Value Value change, % Rank
Bangladesh Bangladesh 76 +11.3% 8
Central African Republic Central African Republic 76 -9.85% 8
Cuba Cuba 89.9 2
Algeria Algeria 89.1 +9.33% 3
Guinea-Bissau Guinea-Bissau 32.6 +26.8% 11
Peru Peru 90.8 +2.37% 1
Sierra Leone Sierra Leone 82.2 -3.63% 7
São Tomé & Príncipe São Tomé & Príncipe 88.6 -2.42% 4
Syria Syria 72 +10.1% 9
Chad Chad 65 -15% 10
Thailand Thailand 84.1 -0.708% 5
Zimbabwe Zimbabwe 83.8 -10.2% 6

The consumption of iodized salt is an essential public health indicator that reflects the efforts made toward ensuring the adequate intake of iodine in the population. Iodine is a vital micronutrient that plays a crucial role in thyroid function, metabolism, and overall health. The consumption of iodized salt, defined as the percentage of households using iodized salt, is a measure of a nation's commitment to preventing iodine deficiency disorders. This article delves into the significance of iodized salt, its relations to other health indicators, factors influencing its consumption, available strategies, and potential solutions while addressing any existing flaws in the data or reporting mechanisms.

The importance of iodized salt cannot be overstated, as iodine deficiency can lead to various health issues, including goiter, intellectual disabilities, and other cognitive impairments. In light of this, health authorities worldwide have launched initiatives to increase the availability and consumption of iodized salt. The indicator of iodized salt consumption reflects not only public health efforts but also disparities in health education, economic status, and access to resources. Monitoring the percentage of households consuming iodized salt provides insights into the progression of these health initiatives and highlights areas needing further attention.

In 2020, the data indicates a median value of 95.7% for the consumption of iodized salt, with reported figures specifically citing the Palestinian Territories. This figure signifies that a vast majority of households in this area rely on iodized salt in their daily dietary practices. Despite the median value appearing promising, it is worth noting that the same figure is recorded for both the top and bottom five areas within this context, leading to the conclusion that while there is a high level of iodized salt consumption within the Palestinian Territories, the lack of variance points to a potential limitation in the dataset. It raises questions about the accuracy and granularity of reporting across diverse regions, suggesting that while a general trend exists, localized data could illuminate distinct regional challenges and successes in iodization efforts.

This single data point, while valuable, lacks comparative world values, raising concerns about understanding the global context of iodized salt consumption. Insights from broader datasets could help identify trends across different nations and regions while also drawing attention to areas where iodized salt consumption may be significantly lower than the median. Additionally, the disparities across various countries and socioeconomic contexts reveal how intertwined public health policies, food security, education, and access to health resources can shape dietary practices and nutritional outcomes.

When analyzing the consumption of iodized salt, several factors come into play, influencing its prevalence in households. Economic stability plays a crucial role, as low-income families may struggle to afford iodized salt compared to non-iodized alternatives. Education is another critical factor; communities with increased awareness of the benefits of iodine are more likely to utilize iodized salt. Furthermore, cultural practices and culinary traditions may impact the choice of salt, where households inclined to traditional food preparation may forgo iodized salt for more locally sourced minerals. The availability of iodized salt in local markets also matters, as transportation and distribution challenges in certain regions could hinder access.

To enhance the consumption of iodized salt, effective strategies must be implemented. Raising public awareness through education campaigns can significantly contribute to the understanding of iodine deficiency’s implications and the benefits of iodized salt consumption. School programs serving children can be pivotal in imparting knowledge about nutrition and health while ensuring that meals served in educational settings incorporate iodized salt as a standard. Strengthening regulatory frameworks can also serve to ensure that iodized salt is more accessible and available in the market through subsidies or incentivizing local producers to prioritize preparatory practices that promote iodization.

Solutions to increase iodized salt consumption must also focus on community engagement, encouraging local stakeholders to advocate for its benefits actively. Collaboration between governments, NGOs, and international organizations is key in designing programs geared towards fiscal, educational, and infrastructural support for iodization initiatives. Moreover, monitoring and evaluation are crucial components of any successful intervention, allowing for the assessment of progress while making necessary adjustments based on local needs and data.

Despite the apparent benefits associated with iodized salt consumption, flaws do exist. The data provided, particularly the staticity observed in the top and bottom areas, suggests a need for more nuanced data collection methodologies. Over-reliance on singular data points can obscure the true eating habits and environmental factors that affect iodized salt consumption. Addressing these gaps may entail comprehensive surveys that include demographic, socioeconomic, and behavioral factors, leading to a more thorough understanding of salt consumption patterns.

In conclusion, while the consumption of iodized salt serves as a vital health indicator and reflects successful public health strategies, continuous effort is needed to ensure that it remains high across diverse communities. By addressing the limitations within current data collection methods and implementing targeted strategies to enhance education and access, the goal of universal consumption of iodized salt can be made more attainable. The ultimate aim is to safeguard against iodine deficiency and foster a healthier population, underscoring the fact that even small dietary changes can lead to significant public health benefits.

                    
# 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 = 'SN.ITK.SALT.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 <- 'SN.ITK.SALT.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))