Prevalence of anemia among pregnant women (%)

The prevalence of anemia among pregnant women is a critical public health indicator, reflecting not only maternal health but also the overall well-being of populations. Anemia in pregnancy, characterized by low hemoglobin levels, can result in numerous adverse outcomes for both mothers and their infants. The latest data from 2019 shows a median prevalence of 27.7% among pregnant women worldwide, highlighting a significant health concern in various regions. This figure ought to be contextualized with the broader implications of anemia. Low iron levels can affect a mother's ability to cope with the physiological demands of pregnancy, potentially leading to complications such as preterm delivery, low birth weight, and increased maternal morbidity. The importance of addressing this indicator extends beyond maternal health to influence child health outcomes; children born to anemic mothers are at increased risk of developmental issues and mortality during the neonatal period. Analyzing the data reveals stark geographical disparities in the prevalence of anemia among pregnant women. The highest rates are observed in Mali (59.0%), Benin (58.1%), Yemen (57.5%), Senegal (56.1%), and Nigeria (55.9%). These regions share characteristics such as high rates of poverty, limited access to healthcare, and inadequate nutrition, all of which contribute to the prevalence of anemia. These nations face significant challenges related to the availability of iron-rich foods, effective healthcare services, and maternal education concerning nutrition and anemia prevention. In contrast, areas with the lowest prevalence of anemia among pregnant women are the United States (11.5%), Guatemala (12.7%), South Korea (14.7%), Switzerland (14.9%), and Luxembourg (15.0%). These countries showcase advanced healthcare systems, better access to a varied diet, and public health initiatives emphasizing maternal health. This contrast underscores the critical relationship between socioeconomic status, healthcare access, and nutritional education in influencing anemia rates. Globally, the trend from 2000 to 2019 demonstrates a slow but notable decline in the prevalence of anemia among pregnant women, reducing from a high of 40.9% in 2000 to 36.5% in 2019. Although this indicates progress in some regions, the median prevalence still signifies a public health challenge. Such slow progress suggests that while initiatives to reduce anemia are being implemented, there remains much work to be done to reach lower prevalence levels that can ensure safer pregnancies. Several factors affect the prevalence of anemia among pregnant women, including dietary practices, socioeconomic conditions, and cultural beliefs. Nutritional deficiencies, particularly in iron, folate, and vitamin B12, can increase vulnerability to anemia. Socioeconomic factors, including income levels and education about nutrition, can also play a crucial role. For instance, women with higher educational attainment are often more aware of the health implications of anemia and may take proactive measures to prevent it during pregnancy. Strategies to reduce anemia prevalence among pregnant women must encompass a multifaceted approach. Public health programs should focus on enhancing maternal nutrition through education, supplementation programs targeting vulnerable populations, and improving access to healthcare services. Iron supplementation and nutrition education can effectively lower the rates of anemia, particularly in low-resource settings. Moreover, policies aimed at improving food security and access to iron-rich foods can mitigate the risk of anemia. Collaborative efforts involving governments, NGOs, and health organizations can lead to comprehensive strategies that address the various underlying causes of anemia. Despite the existing knowledge and strategies for addressing anemia in pregnant women, flaws remain in the execution of these solutions. In many low-income countries, healthcare systems are overstretched, and insufficient resources may lead to poor implementation of nutritional programs. Additionally, cultural barriers and misinformation about dietary needs can hinder the effectiveness of public health campaigns. There is also a need for ongoing research to continually adapt and target interventions to meet the ever-changing demographics and health needs of populations. In conclusion, the prevalence of anemia among pregnant women is more than just a statistic; it reflects broader health, societal, and economic issues. Addressing anemia requires a coordinated effort that combines education, health policy, and community engagement to achieve better health outcomes for mothers and infants alike. The data from 2019 serves as a reminder of the journey ahead in ensuring that every pregnant woman has access to the resources needed to overcome anemia, fostering healthier pregnancies and improved infant well-being.

                    
# 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.PRG.ANEM'

# 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.PRG.ANEM'

# 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))