Suicide mortality rate, male (per 100,000 male population)

Suicide is a critical public health issue globally, and understanding the suicide mortality rate among males is essential for formulating effective strategies and policies aimed at reducing these tragic incidents. The suicide mortality rate, expressed as deaths per 100,000 male population, provides a clear picture of how prevalent this issue is within different regions and demographics. In 2019, the global median value for male suicide mortality was 11.3, indicating that roughly 11 males per 100,000 took their own lives. This figure, while seemingly moderate, represents deep societal issues and underlying challenges that require comprehensive approaches to address.

Examining the top countries with the highest male suicide rates presents a stark contrast to the global average. Lesotho leads with an alarming rate of 116.0, followed by Guyana at 63.0, Eswatini at 55.1, Kiribati at 48.6, and Lithuania at 45.4. These figures reflect not only the mental health crises prevalent in these areas but also underline potential social and economic challenges, including poverty, unemployment, and a lack of mental health resources. For instance, in Lesotho, factors such as high rates of HIV/AIDS, socio-economic instability, and insufficient healthcare infrastructure contribute significantly to these tragic outcomes.

On the other end of the spectrum, the bottom five areas in terms of male suicide rates—Antigua & Barbuda (0.0), Grenada (0.6), Barbados (0.9), St. Vincent & the Grenadines (1.3), and São Tomé & Príncipe (2.2)—demonstrate much lower instances of suicide. The stark difference between these regions may be attributed to various factors, including robust community support systems, effective mental health services, or socio-economic conditions that promote greater well-being. In countries like Antigua & Barbuda, the tight-knit communities and focus on family-oriented social interactions could contribute to the lowering of suicide rates.

To contextualize the data across years, it is essential to observe the trend in the global suicide mortality rate for males from 2000 to 2019. The rate declined from 16.69 in 2000 to 12.57 in 2019. This gradual decline is a positive indication of increased awareness and intervention regarding mental health issues over the years. Improved accessibility to mental health care, progressive attitudes towards mental health challenges, and increased education surrounding this topic are likely contributors to this downward trend. However, despite this overall reduction, specific regions remain profoundly affected, highlighting the need for targeted efforts.

The factors influencing suicide mortality rates among males are multifaceted. Social determinants of health—such as economic status, education, family dynamics, and cultural stigma surrounding mental health problems—play crucial roles in influencing an individual’s propensity for suicidal behavior. Additionally, substance abuse, particularly alcohol, has been strongly linked to increased suicide rates. Males, who might be less likely to seek help for mental health issues due to societal expectations around masculinity, may turn to alcohol or drugs as coping mechanisms, thereby exacerbating suicidal tendencies.

Considering the complexities underpinning male suicide rates, effective strategies must be implemented across multiple levels. Community-based programs aimed at improving mental health literacy and promoting help-seeking behavior are essential in combating stigma. Strategies could include school-based initiatives that teach coping mechanisms and emotional resilience from a young age, family therapy to foster open communication, as well as extensive outreach programs targeting high-risk areas such as Lesotho or Guyana.

Moreover, it is imperative that policies promote accessible mental health services, incorporating trained professionals who understand the unique challenges that males face. Investments in healthcare infrastructure are vital to ensure that mental health resources are available, particularly in regions displaying high suicide rates. Collaboration between governments, NGOs, and community organizations can facilitate a more robust support network that encourages individuals to seek help before reaching crisis points.

While progress has been made in recent years regarding the overall suicide rate among males, flaws in data collection and reporting processes can impact the authenticity of the statistics. In several countries, particularly those with limited healthcare infrastructure and social stigma surrounding mental health, the true rates of suicide may be underreported. This underreporting complicates the effective allocation of resources and the establishment of need-based intervention strategies.

In conclusion, the suicide mortality rate among males serves as a crucial indicator of societal health and well-being. The stark differences between regions offer insights into the various factors influencing these rates, from socio-economic conditions to healthcare access and societal norms. With targeted strategies, open communication, and comprehensive mental health resources, we can continue to work towards reductions in these rates, ultimately fostering environments where mental health is prioritized, and lives are saved.

                    
# 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.MA.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.MA.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))