Cause of death, by injury (% of total)

The indicator 'Cause of death, by injury (% of total)' reflects the proportion of deaths resulting from injuries, which encompasses a wide range of fatal incidents such as road traffic accidents, falls, drowning, and interpersonal violence. This metric serves as a critical gauge of societal safety and health outcomes, significantly impacting public health planning, resource allocation, and the formation of safety policies. By analyzing injury-related deaths, we can discern patterns and risk factors that contribute to public health challenges and work towards mitigating them.

The importance of monitoring the percentage of deaths due to injury lies in its ability to reveal underlying issues within specific populations and regions. Higher percentages indicate a greater burden of injuries, often associated with factors like socio-economic development, effective governance, infrastructure quality, and healthcare access. Monitoring this indicator allows governments and health organizations to identify regions where interventions are required, promoting the development of targeted strategies to enhance safety and reduce mortality rates.

In tracking trends over time, it is evident that the global average for injury-related deaths has fluctuated. In 2000, the world value stood at 8.55% of total deaths by injury, rising to 8.86% in 2010. However, this figure saw a slight decline to 8.3% in 2015, leading to an even lower value of 7.96% in 2019. These fluctuations suggest a complex interplay of factors such as improved healthcare, public safety initiatives, and variations in conflict or violence incidence across different regions.

A closer look at nations showcasing the highest percentages of deaths due to injuries reveals concerning trends indicative of broader public health challenges. For example, in 2019, Yemen recorded a staggering 19.49%, followed closely by Saudi Arabia at 19.46%, and Venezuela at 19.38%. Such high injury mortality rates may be reflective of ongoing conflicts, high levels of violence, and insufficient healthcare systems. In these regions, trauma from war, civil disturbance, and crime contributes to high death tolls from injury. Similarly, El Salvador and Belize, both with injury-related death percentages over 18%, highlight the impact of violent crime, particularly gang-related violence, underscoring the urgent need for comprehensive societal reforms and action to ensure safety and improve healthcare systems.

Conversely, the bottom five regions, including Serbia with a low of 2.37% and Bulgaria at 2.47%, illustrate the effectiveness of strategic public health interventions and civil engagement. These nations often feature more robust health and safety regulations, public awareness campaigns, and investments in emergency services. Such lower percentages reflect a social environment more adept at preventing injuries and managing their consequences when they do occur. These examples emphasize the correlation between injury mortality rates and broader indicators of societal well-being, such as economic stability, education, and healthcare quality.

A variety of factors can influence the percentages of deaths due to injury in any given area. These include socio-economic status, the prevalence of violent crime, urbanization trends, road traffic regulations, and access to healthcare. Low-income areas may frequently witness higher injury mortality rates due to inadequate infrastructure, limited access to preventative education, and insufficient healthcare services. Additionally, cultural attitudes towards injury and violence play a critical role. Areas with a strong cultural acceptance of violence or inadequate public health responses may face higher injury-related deaths.

Addressing the causes of deaths by injury must involve multi-faceted strategies tailored to local contexts. Initiatives can range from enhancing law enforcement capacities and road safety regulations to implementing educational campaigns promoting safety behaviors. Communities can benefit from building resilience through improvements in social programs aimed at reducing violence and increasing economic opportunities. Collaborative partnerships among governments, NGOs, and healthcare providers are essential to creating sustainable solutions that not only focus on reactive measures but also emphasize preventative strategies.

Despite the potential for improvement, challenges persist in effectively reducing injury-related death rates. One significant flaw in current frameworks is the inconsistency in data collection and reporting methodologies, leading to discrepancies across regions. Some nations may underreport due to inadequate tracking or stigma associated with violence, thereby skewing perceptions of injury death rates. Moreover, immediate responses to violence may overshadow long-term strategies aimed at addressing the root causes of injury rates in various settings.

Ultimately, understanding the cause of death by injury as a percentage of total deaths is vital not only for creating responsive public health policies but for assessing overall societal health. As we evaluate shifts in trends over time and across geographic regions, it becomes apparent that collaborative, community-centric approaches combined with effective governance can lead to meaningful reductions in injury-related mortalities. By investing in data infrastructure, public education, and rehabilitation initiatives, we can protect communities and enhance resilience toward preventing injury-related deaths 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.DTH.INJR.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.DTH.INJR.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))