Number of surgical procedures (per 100,000 population)

Source: worldbank.org, 01.09.2025

Year: 2011

Flag Country Value Value change, % Rank
Bangladesh Bangladesh 162 7
Ethiopia Ethiopia 43 8
Myanmar (Burma) Myanmar (Burma) 668 4
Nepal Nepal 208 6
Peru Peru 3,005 3
Portugal Portugal 8,439 1
Uganda Uganda 241 5
Ukraine Ukraine 5,337 2

The indicator representing the number of surgical procedures performed per 100,000 population is an essential metric reflecting healthcare delivery effectiveness, population health, and the availability of surgical services within specific regions. Tracking and analyzing this data provides insights into the healthcare systems' capabilities, the population's health needs, and the overall efficiency of surgical care delivery. In 2023, the median value for surgical procedures performed globally stood at 4343.85, indicating that on average, many regions are engaging in extensive surgical practices. Understanding this figure is crucial for shaping health policy and resource allocation.

The importance of this indicator lies in its ability to reveal disparities in healthcare access. Regions with a high number of surgical procedures tend to have better healthcare infrastructures, access to trained healthcare professionals, and financial means to support advanced medical technologies. Conversely, areas with lower rates often face challenges, including inadequate healthcare facilities, a shortage of trained surgeons, or financial barriers that limit residents' ability to seek needed surgical interventions.

Examining the data from the top areas, Spain, with a staggering 6953.2 surgical procedures per 100,000 population, exemplifies a region characterized by robust healthcare resources and a strong emphasis on preventative and elective surgery. This high number suggests that the healthcare system in Spain is well equipped to handle not just emergent cases but also elective surgeries, which are vital for improving the quality of life. Following closely is Hong Kong SAR, China, with a figure of 1734.5. While this number is significant, it indeed points to disparities within the healthcare system compared to Spain, revealing differences in healthcare access, economic factors, or perhaps the demographics of the population.

The bottom areas reveal the complexities involved in analyzing surgical data. Interestingly, Hong Kong's figure appears as both a top performer and a bottom performer due to the way the data is organized, indicating where there might be overlaps in analysis. The confusion in classification underscores the necessity to correctly interpret these numbers. This aspect reflects not only on the healthcare infrastructure but also on policy frameworks that may enhance or hinder surgical care delivery.

A key relation between surgical procedure volume and other health indicators can be seen when considering morbidity and mortality rates. Areas with higher surgical procedure rates generally correlate with lower mortality rates from conditions that may require surgical intervention. Access to surgical services can act as a decisive factor in recovery from acute illnesses or serious medical conditions. Moreover, countries with strong surgical networks often exhibit better metrics in life expectancy and overall health outcomes. Thus, monitoring this indicator strings together various aspects of healthcare delivery, efficiency, and population health.

Several factors affect the number of surgical procedures performed in a given area. Economic conditions are paramount; regions with higher levels of income can allocate a more significant portion of financial resources towards healthcare. Additionally, the demographic composition of populations, including age and prevalence of diseases requiring surgical intervention, also play essential roles. Moreover, cultural attitudes towards surgery, availability of advanced medical technology, and the presence of healthcare professionals are equally influential in shaping surgical statistics.

Efforts to improve the surgical procedure rate call for multifaceted strategies that masterfully integrate policy, education, and resource management. Establishing comprehensive training programs for healthcare professionals ensures a broader base of qualified surgeons across various demographics. Furthermore, investing in healthcare infrastructure and technology can help bridge the gap between high and low performing regions. Coverage by health insurance systems must also expand to ensure patient access to surgical options, minimizing financial barriers.

Despite acknowledging the importance of surgical procedure rates, flaws and limitations exist in the interpretation of this data. First, the numbers can be influenced by systemic biases, such as over- or under-reporting in certain regions or healthcare settings. This inconsistency can distort calculations, leading to misleading conclusions regarding the quality of healthcare systems. Additionally, not all surgical procedures are equal; variations in complexity and necessity can often lead to skewed data. For example, some regions may prioritize emergency surgeries over elective ones due to resource constraints, impacting their surgical procedure statistics significantly.

In conclusion, the number of surgical procedures per 100,000 population serves as a crucial indicator of healthcare effectiveness, resource distribution, and overall public health. By analyzing this metric and understanding its broader implications, stakeholders can devise strategies to optimize surgical services, improve health outcomes, and promote equitable access to necessary procedures. As we advance into future healthcare discussions, maintaining a focus on improving this indicator will contribute significantly to the health and well-being of populations worldwide.

                    
# 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.SGR.PROC.P5'

# 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.SGR.PROC.P5'

# 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))