Cause of death, by non-communicable diseases (% of total)

The indicator "Cause of death, by non-communicable diseases (% of total)" serves as a crucial measure of health outcomes linked to chronic conditions such as heart disease, stroke, cancer, diabetes, and respiratory diseases. Understanding this indicator not only sheds light on the overall health status of populations but also highlights the underlying risk factors that contribute to health disparities across different regions. The latest data from 2019 indicates a global movement towards prioritizing the management of non-communicable diseases (NCDs), with a median value of 77.86%. This figure is significant, reflecting an increasing recognition of the burden NCDs impose on healthcare systems worldwide.

Non-communicable diseases have been a growing public health concern, especially as life expectancy rises and lifestyles shift towards more sedentary habits combined with unhealthy diets. In many high-income countries, NCDs account for the majority of deaths, fuelling health policy discussions and investment strategies aimed at prevention and management. The stark contrast between regions also illuminates disparities in healthcare access, socioeconomic conditions, and public health initiatives.

When examining the top five areas with the highest percentages of deaths due to NCDs, we see North Macedonia leading with 96.13%, followed closely by Bulgaria (95.31%), Serbia (94.92%), Bosnia & Herzegovina (94.42%), and Montenegro (94.11%). Such high values are indicative not only of the maturity of healthcare systems that have made progress in managing infectious diseases but also of lifestyle-related health risks that have become more prevalent. These countries typically face challenges such as high rates of smoking, poor dietary habits, and inadequate physical activity among their populations, all of which are significant contributors to the prevalence of NCDs.

On the other end of the spectrum lies the bottom five areas: Chad (26.99%), Nigeria (27.13%), South Sudan (27.94%), Somalia (29.88%), and Mali (30.32%). The relatively low percentages of NCD-related deaths in these regions can be misleading, as they may be more reflective of the prevalence of communicable diseases, which are still leading causes of death due to factors like poverty, limited access to healthcare, and higher childhood mortality rates. Moreover, these nations may not yet have developed the necessary infrastructure or public health policies aimed at addressing chronic health conditions. The limited data availability and underreporting in these countries also contribute to the discrepancy in understanding true mortality causes and their impacts.

The evolution of global health trends shows that the global percentage of deaths from NCDs has steadily risen over the decades. In 2000, the percentage stood at 60.8%, climbing to 67.63% by 2010, and further increasing to 71.22% in 2015 before reaching 73.63% in 2019. This gradual rise indicates a widening gap in health outcomes between different populations and underlines the urgent need for targeted health interventions to manage and prevent NCDs effectively.

The importance of addressing NCD-related deaths lies in the significant impact these diseases have not only on individual lives but also on economies and societal structures. Non-communicable diseases can create a cycle of health-related issues that strain limited healthcare resources, reduce workforce productivity, and escalate healthcare costs. Understanding causes of death by NCDs provides valuable insights for policymakers aiming to allocate resources effectively and implement appropriate health interventions.

Several factors influence the incidence and mortality of non-communicable diseases. Lifestyle factors including poor nutrition, physical inactivity, tobacco use, and excessive alcohol consumption are major contributors to the risk of developing NCDs. Additionally, social determinants such as education, socioeconomic status, and access to healthcare services play a critical role in determining health outcomes. The interplay between lifestyle choices and socio-economic conditions necessitates a multifaceted approach to health promotion and disease prevention.

Strategies to mitigate the effects of non-communicable diseases should encompass public health initiatives that promote healthier lifestyles. This includes policies aimed at reducing tobacco and alcohol consumption, improving nutritional standards, and encouraging physical activity among communities. Regular health screenings can aid early detection, allowing for timely intervention that can significantly impact health outcomes. Furthermore, enhancing access to healthcare services, particularly in underserved regions, is essential in tackling the burden of NCDs.

While there has been notable progress in understanding and addressing non-communicable diseases, several flaws still exist in the current public health framework. One such flaw is the tendency to under-prioritize NCDs in favor of communicable diseases, especially in lower-income areas where infectious diseases remain a pressing concern. This can result in unequal distribution of resources and neglect of preventive strategies necessary for reducing NCD incidence. Additionally, gaps in data collection and reporting hinder effective monitoring of mortality rates and can distort perceptions regarding the true burden imposed by NCDs.

In conclusion, the indicator of deaths caused by non-communicable diseases highlights critical aspects of population health across the globe. The disparity in NCD-related mortality underscores the urgent need for comprehensive public health strategies that consider both preventative measures and treatment options tailored to the specific needs of populations. By focusing on NCD prevention and promoting healthier lifestyles, countries can work towards improving health outcomes and reducing the economic burden associated with these diseases.

                    
# 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.NCOM.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.NCOM.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))