Death rate, crude (per 1,000 people)

The crude death rate, expressed as the number of deaths per 1,000 people in a given population, serves as a vital indicator of a country's overall health, social conditions, and the effectiveness of its healthcare system. This metric provides insights not only into the mortality patterns in a specific region but also into the underlying factors influencing these patterns. In 2022, the world median crude death rate was recorded at 7.83, reflecting an overall trend of improving public health over the decades, although it remains a crucial factor in understanding demographic shifts.

The significance of the crude death rate extends beyond the simple count of how many people die. It is a crucial component in assessing life expectancy, population growth, and public health policies. A high crude death rate can be indicative of several challenges, including inadequate healthcare access, high rates of infectious diseases, malnutrition, and social upheaval. Conversely, a lower crude death rate often correlates with better healthcare systems, economic stability, and overall improved living conditions across the populace.

Examining the data from 2022 reveals stark contrasts between various regions. Notably, Ukraine tops the list with a crude death rate of 21.4, significantly higher than the median value. This high figure can likely be attributed to ongoing conflict, economic hardship, and the resultant stress on healthcare services. Similarly, Bulgaria (18.4), Moldova (16.61), Latvia (16.4), and Serbia (16.2) exhibit higher death rates, often linked to aging populations, declining birth rates, and emigration trends that affect the demographics positively and negatively.

On the other end of the spectrum, countries like Qatar (1.08), the United Arab Emirates (1.86), Bahrain (2.48), Kuwait (2.61), and Saudi Arabia (2.75) showcase remarkably low crude death rates. These countries benefit from advanced healthcare systems, substantial investment in health infrastructure, and a younger population profile, which collectively contribute to lower mortality rates. The variance between these top and bottom regions illustrates how sociopolitical contexts and economic investment can shape public health outcomes.

The crude death rate is closely related to several other demographic indicators, such as birth rates, migration patterns, and life expectancy. For instance, a low crude death rate in a country often mirrors a low fertility rate, as a declining birth rate may be indicative of enhanced family planning and women’s health education. Migration trends also play a crucial role; countries experiencing an influx of young, healthy individuals may report lower death rates than those with aging populations and higher rates of emigration.

Several factors can affect the crude death rate, including healthcare access, socioeconomic status, lifestyle choices, and environmental conditions. For instance, access to quality healthcare can drastically reduce mortality from treatable diseases. Socioeconomic factors, including income levels, education, and occupation, also influence health outcomes. A well-educated population with access to healthcare tends to have lower mortality rates. Lifestyle choices, such as diet, exercise, and smoking, play a significant role in public health, affecting chronic disease prevalence and, subsequently, death rates.

To address high crude death rates, various strategies can be employed by governments and health organizations. Investments in healthcare infrastructure and increased access to essential medical services can significantly reduce mortality rates. Public health campaigns focusing on education about disease prevention, nutrition, and healthy lifestyles can foster a more health-conscious society. Furthermore, policies aimed at improving the living conditions, such as tackling poverty and providing better social services, are essential to reducing mortality.

However, while strides can be made in reducing crude death rates, some flaws in the methodology of calculating and interpreting these rates come to the forefront. For instance, the crude death rate does not account for the age distribution of a population; thus, countries with larger elderly populations may have higher crude death rates despite effective healthcare systems. Additionally, in regions affected by conflict or natural disasters, the data may not accurately reflect the real situation due to underreporting of deaths or disruptions in public health services.

In conclusion, the crude death rate serves as a fundamental barometer for assessing public health and the socioeconomic conditions of a nation. While global trends show some progress toward lower mortality rates, challenges remain, particularly in regions burdened by conflict or economic instability. Understanding this indicator in a broader context, considering its relations to other demographic indicators and focusing on both preventive and reactive measures, is crucial for fostering healthier 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 = 'SP.DYN.CDRT.IN'

# 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 <- 'SP.DYN.CDRT.IN'

# 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))