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

The suicide mortality rate among females, expressed as the number of suicides per 100,000 female population, stands as a crucial indicator of public health and societal well-being. This statistic provides a window into the mental health landscape facing women across different regions, serving not only as a reflection of individual crises but also as a revealing metric that can expose deeper societal issues such as stigma, access to mental health care, and gender-based discrimination. Understanding the complexities of this indicator is essential for developing effective policies and programs aimed at prevention and support.

As of 2019, the global average suicide mortality rate for females was recorded at 5.68 per 100,000. While this may seem like a mild statistic on the surface, it encapsulates a multitude of stories of despair and mental health struggles endured by countless women. The median value recorded globally, standing at 3.3, hints at a concerning disparity in mental health outcomes for women, significantly differentiating between countries that provide adequate mental health resources and those that do not.

In examining the top five areas with the highest female suicide rates, a stark and troubling picture emerges. Lesotho leads with an extraordinarily high rate of 30.1, indicating critical mental health and socioeconomic deficits that warrant urgent attention. Guyana, South Korea, Micronesia, and Belgium follow with rates of 17.4, 16.9, 12.7, and 11.8, respectively. The disproportionate rates in these regions suggest various risk factors at play, including high levels of violence, poverty, limited access to mental health services, and societal pressures, particularly for women. In contrast, the bottom five areas reflect a markedly different reality; countries like Barbados (0.3), St. Vincent & Grenadines (0.6), and Grenada (0.7) showcase some of the lowest female suicide rates globally, indicating more effective intervention strategies or cultural factors promoting mental well-being.

The relationship between suicide mortality rates and other public health indicators is robust and complex. For instance, high suicide rates are often correlated with elevated levels of depression and anxiety within a population, suggesting that mental health services, community support systems, and educational outreach programs can play critical roles in preventing these tragedies. Furthermore, issues such as economic instability, substance abuse, and violence against women can create an environment where mental health deteriorates rapidly, leading to increased risks of suicide among females.

Several factors contribute to the suicidal behavior of females in various regions. These include but are not limited to socio-economic status, access to healthcare, societal norms, and cultural attitudes towards mental health. In many countries, women face unique societal pressures, including those stemming from traditional gender roles and expectations that can exacerbate feelings of isolation and hopelessness. Furthermore, the stigma associated with mental health issues often prevents individuals from seeking help, significantly impacting various demographics, particularly women who might prioritize familial responsibilities over personal wellness.

To address and mitigate the high suicide rates among females, a multifaceted approach is necessary. Strategies may include enhancing mental health services, providing education and resources aimed at destigmatizing mental health illnesses, and developing community support systems. Public awareness campaigns can inform women about available help, encouraging them to seek assistance. Furthermore, ensuring that mental health screenings are a routine part of health assessments in both schools and workplaces can identify at-risk individuals before crises develop.

Moreover, creating supportive environments through community programs that focus on mental wellness and resilience can be invaluable. Localized efforts that adapt to cultural contexts may contribute to improving mental health outcomes for women. Training for primary healthcare providers to recognize and address mental health issues can also create a more efficient referral system for those needing specialized care.

However, it is crucial to note some flaws in the existing methodologies used to assess this indicator effectively. Data collection methods can be inconsistent across different countries and regions, leading to disparities in reported figures. Some regions may suffer from underreporting due to stigma or inadequate health infrastructure, resulting in skewed statistics. Moreover, the lack of a unified framework for categorizing and defining suicide can complicate the global comparison of rates.

In summation, the suicide mortality rate among females is more than a figure; it is a profound call to action that highlights the need for concerted efforts to address mental health disparities. Understanding its roots and implications remains paramount for not just reducing the rates of female suicides but for creating a society alive with hope, support, and mental well-being. As communities and policymakers strive to address this crisis, it is essential to remember that every statistic is a life lost or saved—this personhood is at the heart of any effective strategy against suicide mortality rates.

                    
# 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.FE.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.FE.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))