Prevalence of anemia among non-pregnant women (% of women ages 15-49)

The prevalence of anemia among non-pregnant women aged 15-49 is a crucial indicator of public health, reflecting broader issues related to nutrition, healthcare access, and socioeconomic conditions. Anemia, defined as a deficiency in the number or quality of red blood cells, can severely impact women's health, leading to fatigue, weakness, and reduced physical and cognitive capabilities. This condition is particularly critical among women within the reproductive age, as it can affect their overall well-being and, if they become pregnant, the health outcomes for their children.

The importance of monitoring the prevalence of anemia lies in its correlations to various other health and socioeconomic indicators. High rates of anemia often accompany high rates of poverty, limited access to healthcare, and inadequate nutritional education. Thus, this indicator not only serves as a direct measure of women's health but also as a reflective tool for assessing the overall health infrastructure and socioeconomic conditions of a region. For example, in countries with high rates of anemia, such as Yemen and Mali, we frequently observe intertwined challenges like malnutrition, limited access to iron-rich foods, and insufficient healthcare resources.

Among the statistics for 2019, the median value of anemia prevalence stood at 24.85%. This figure indicates a significant public health concern, especially when comparing it to the data from previous years, which showed that the global prevalence of anemia among women in this demographic has been consistently high since 2000. For instance, in 2000, the prevalence was noted at 30.7%, and while it saw a gradual decline through to 2019, where it reached 29.6%, the decline is not substantial enough to proclaim a victory against the war on anemia.

Focusing on geographical disparities reveals stark contrasts worldwide. The top five areas with the highest reported prevalence of anemia among non-pregnant women all exceed 50%, highlighting the acute nature of the problem in these regions. Yemen leads at an alarming 61.8%, followed by Mali at 59.0%, Nigeria at 55.0%, Benin at 54.8%, and India at 53.1%. These figures suggest multifactorial causes, including poor dietary intake, epigenetic factors, infection prevalence (especially malaria and hookworm), and socio-economic barriers that impede access to essential healthcare services.

Conversely, the bottom five areas reveal a much more favorable situation, with Guatemala at only 7.0%, Australia at 8.2%, Chile at 8.4%, Iceland at 10.1%, and Luxembourg also at 10.1%. The stark difference between the high-prevalence and low-prevalence regions indicates that solutions are available and that socioeconomic development, public health initiatives, and nutrition education can substantially influence outcomes. Factors affecting anemia are multifaceted. Nutritional inadequacies, particularly iron-deficiency anemia, contribute significantly, exacerbated by absorption issues in the gut due to intestinal parasites, chronic infections, and genetic conditions such as thalassemia and sickle cell anemia.

Strategies to combat anemia focus primarily on improving access to nutrition and healthcare services. Governments and organizations can implement programs aimed at increasing the availability of iron-rich foods and supplements. Education plays a key role as well; fostering community awareness around proper nutrition and how to identify food sources of iron and vitamins can empower women to make informed dietary choices.

Moreover, regular health screenings and anemia management programs, especially in high-prevalence areas, must be prioritized. Collaboration with local healthcare providers to ensure access to medical treatment for underlying causes, such as infections or menstrual disorders, is likewise critical. Public health campaigns that encourage routine check-ups and the importance of iron intake can significantly reduce the prevalence of anemia over time.

Despite these strategies, there are inherent flaws and challenges in effectively addressing anemia. Proposals must consider cultural perceptions of health and nutrition, as well as the economic realities that limit access to diverse diets rich in essential nutrients. Political instability in certain regions hampers the delivery of health services and creates barriers to effective public health interventions. Additionally, the apathy to anemia may result in underdiagnosis or misdiagnosis, further exacerbating the problem.

A holistic approach is vital to overcome these challenges. This includes cross-sector partnerships that integrate health, education, and food security initiatives. Addressing anemia requires a commitment from governmental and non-governmental organizations, community leaders, and women themselves to collaboratively strive for better health outcomes. By continuing to monitor and analyze anemia prevalence, countries can tailor strategies that not only address the immediate needs of non-pregnant women but also contribute to more robust health systems and improved quality of life across generations.

In summary, the prevalence of anemia among non-pregnant women aged 15-49 reveals a significant public health issue that is both a consequence and a predictor of socio-economic challenges. By harmonizing efforts across sectors and focusing on effective implementation of nutrition and healthcare solutions, significant progress can be made to mitigate this pervasive problem.

                    
# 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.ANM.NPRG.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.ANM.NPRG.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))