Suicide mortality rate (per 100,000 population)

The suicide mortality rate, expressed as the number of suicides per 100,000 population, is a crucial health indicator that reflects the mental health and overall well-being of a population. With a median value of 7.5 in 2019, this statistic serves as a barometer for societal distress and functionality, capturing the levels of despair and mental health crises faced by different communities. This metric is indicative not only of individual tragedies but also of broader social issues, making it essential for policymakers, healthcare providers, and communities to address.

The importance of tracking suicide mortality rates cannot be overstated. High rates can indicate underlying mental health issues, substance abuse problems, or socio-economic challenges that need to be addressed. Conversely, low rates, as seen in some Caribbean nations where Antigua & Barbuda reported a low rate of 0.4, suggest a healthier population or perhaps a robust support system for mental health. These insights are invaluable for tailoring mental health interventions and resources effectively.

Furthermore, suicide mortality rates often correlate with other socio-economic indicators such as unemployment rates, income inequality, and access to mental health services. For example, the high suicide rates in Lesotho (72.4), Guyana (40.3), and Eswatini (29.4) may reflect not only individual despair but also systemic issues such as poverty, political instability, and inadequate healthcare systems. By interlinking these data points, we can gain a deeper understanding of the societal conditions that contribute to these alarming numbers.

Various factors affect the suicide mortality rate, ranging from individual psychological conditions to broader cultural attitudes towards mental health. Cultural stigma surrounding mental health issues in countries like South Korea (28.6), where a high suicide rate persists, can inhibit individuals from seeking help, exacerbating the issue. Other contributing factors include economic stressors, family dynamics, traumatic experiences, and access to means of suicide. It is essential to consider these multifaceted influences when looking at suicide rates.

Addressing the issue of suicide requires comprehensive strategies at various levels. Public awareness campaigns aimed at reducing stigma, increasing access to mental health services, providing training to schools and workplaces, and implementing better healthcare policies can make significant differences. Community programs that promote social connectivity and provide immediate support to individuals in crisis are paramount. Moreover, integrating mental health education into school curricula can help foster resilience and coping strategies among young people, potentially reducing the rate of suicides among future generations.

However, it's crucial to recognize the limitations and flaws in existing models for reporting and analyzing suicide mortality rates. For example, not all countries have accurate reporting mechanisms, leading to underestimation or overestimation of suicide cases. Cultural taboos may also suppress the disclosure of suicide cases, skewing data. Additionally, mental health issues can often manifest in various ways, and their link to suicide may not always be direct or obvious, complicating statistical analysis and interpretation. Moreover, while international comparisons can yield insights, differences in definitions, reporting standards, and cultural attitudes toward suicide can lead to misleading conclusions.

To illustrate the global context, the world suicide mortality rate has shown a downward trend from 12.93 in 2000 to 9.16 in 2019. This decline may suggest improvements in mental health awareness, access to care, or societal conditions, yet the persistence of high rates in specific regions signals the need for continued attention and intervention. These global numbers provide a backdrop against which individual countries can measure their progress or lack thereof.

The stark contrast between the highest and lowest suicide rates highlights the disparities faced by different populations. Lesotho's staggering rate of 72.4 is a cry for urgent attention, reflecting perhaps not just individual despair but a complete systemic failure in mental health support and social stability. On the other end of the spectrum, the low rates in countries like Antigua & Barbuda (0.4) imply successful preventive measures or socio-economic advantages that help protect against such tragedies. It becomes critical for governments and health organizations to analyze these variances, understanding that solutions cannot be one-size-fits-all but rather tailored to the unique challenges and resources of each area.

In conclusion, the suicide mortality rate serves as a pivotal societal indicator that encapsulates a myriad of influences. Its implications go far beyond statistical values, reflecting the mental health landscape of communities worldwide. By recognizing the importance of these rates, understanding the associated factors, and implementing targeted strategies, global society can work towards not only lowering suicide rates but also improving the overall mental health and quality of life for individuals everywhere.

                    
# 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.STA.SUIC.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.STA.SUIC.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))