Current health expenditure per capita (current US$)

The indicator 'Current health expenditure per capita (current US$)' serves as a crucial measure of a country's investment in health care relative to its population size. This metric is calculated by dividing the total health expenditures of a nation by its population, providing a clear insight into how much each individual is spending or is allocated for health-related services. In 2022, the median value for this indicator across countries was recorded at approximately $4,131.29, highlighting a significant variation in health care investment globally.

The importance of current health expenditure per capita cannot be overstated. It reflects not merely a government's commitment to health but also serves as a proxy for the quality of the healthcare system, and ultimately, the health outcomes of the population. Higher spending in this area typically indicates better access to essential health services, advanced medical technology, and a robust health care infrastructure. It is an essential component in evaluating how health systems respond to the needs of their populations and how effectively they can address public health challenges.

This indicator is closely related to several other health and economic indicators. For instance, it can be correlated with life expectancy, infant mortality rates, and overall population health metrics. Countries that allocate more resources to health typically exhibit higher life expectancy and lower rates of preventable diseases. Moreover, economic indicators such as GDP per capita also impact health expenditures; wealthier nations often allocate more funds to health care, as they can afford better health services for their citizens.

A multitude of factors influence current health expenditure per capita. These include demographic transitions, epidemiological changes, and health system efficiencies. For instance, countries with aging populations often experience increased health care costs due to higher demands for medical services, given that older individuals typically require more frequent and complex health interventions. Additionally, rising chronic diseases prevalence in many nations leads to higher expenditures on long-term care and treatment.

Socioeconomic factors also play a significant role. Nations with lower income levels, such as the Philippines, which reported an expenditure of only $178.00 per capita, struggle to fund comprehensive health care systems. The lack of financial resources not only limits the availability of medical services but also restricts investments in health care infrastructure and workforce development, ultimately affecting health outcomes.

In contrast, the top 5 areas with the highest current health expenditure per capita demonstrate how substantial public and private investments in health care can improve health outcomes. The United States leads the world with $12,473.79, a figure that substantiates its reputation for advanced health technology and extensive health services, albeit shadowed by discussions of access, affordability, and disparities in health outcomes among different population groups. Following the U.S. are Norway ($8,693.00), Luxembourg ($6,956.00), Iceland ($6,400.00), and Ireland ($6,349.48), all of which showcase the correlation between high spending and health outcomes, albeit within the context of each country’s unique health care policies and systems.

Addressing the disparities in health expenditure requires comprehensive and multifaceted strategies. Low-to-middle-income countries need to strengthen their health financing mechanisms by increasing public health budget allocations. Implementing universal health coverage can reduce out-of-pocket spending and protect vulnerable populations. Additionally, investing in health promotion and preventive care can help curb the rising costs associated with chronic diseases, thus improving both public health outcomes and overall expenditure efficiency.

Moreover, international cooperation and guidance are essential for countries struggling with low health expenditure. By establishing partnerships with higher-income nations, lower-income communities can gain access to resources, knowledge, and support systems necessary for improving their healthcare infrastructure and practices.

However, there are inherent flaws in relying solely on current health expenditure per capita as an indicator of health system performance. For one, merely increasing expenditure does not guarantee improved health outcomes. Factors such as healthcare accessibility, quality of care, and socioeconomic disparities must also be addressed to genuinely impact public health positively. Without focusing on the efficiency of how resources are allocated and utilized, there is a risk of wasting funds on ineffective treatments or systems that fail to meet the community's actual health needs.

In summary, the current health expenditure per capita is a vital measure illustrating the commitment of nations to healthcare. It encompasses economic, governmental, and social frameworks shaping health systems globally. As health challenges continue to evolve, so must the strategies to address them, ensuring that every individual has access to optimal health resources for a better quality of life.

                    
# 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.XPD.CHEX.PC.CD'

# 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.XPD.CHEX.PC.CD'

# 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))