Lifetime risk of maternal death (%)

The indicator "Lifetime risk of maternal death (%)" represents the probability that a woman will die from complications related to pregnancy during her lifetime, assuming that she were to experience a lifetime of pregnancies at current rates. This indicator is crucial for evaluating maternal health and the effectiveness of healthcare systems in preventing maternal mortality.

Understanding the lifetime risk of maternal death is important for several reasons. First, it provides insight into the state of women's health within a specific population, indicating how well the healthcare system addresses pregnancy-related complications. A higher lifetime risk often correlates with inadequate access to medical care, poor healthcare infrastructure, and socio-economic factors that hinder women’s health services.

This indicator does not exist in isolation; it relates closely to several other health indicators. For instance, the maternal mortality ratio, which measures the number of maternal deaths per 100,000 live births, is directly linked to this lifetime risk indicator. High maternal mortality ratios generally occur where lifetime risk is elevated, reinforcing the need for targeted interventions. Furthermore, this measure has connections to child health indicators, as the survival of mothers significantly impacts child mortality rates. If mothers do not survive childbirth, the likelihood of infants dying in their first year increases substantially.

Numerous factors contribute to the lifetime risk of maternal death, including socio-economic status, geographic location, access to healthcare services, and education. In regions where poverty is prevalent and educational attainment for women is low, women may lack the knowledge or resources to seek prenatal care, leading to higher risks. Furthermore, countries that experience instability or conflict often see elevated maternal mortality due to disrupted healthcare services and increased risks associated with childbirth in unsafe environments.

Global data reflects an overall improvement in reducing the lifetime risk of maternal death. The median value across the globe in 2020 was recorded at 0.12%. This indicates a notable decline from values recorded at the turn of the millennium, where the global risk was about 0.86%. This decrease showcases the efforts made in global health initiatives aimed at improving maternal healthcare, enhancing access to family planning, and promoting safe childbirth practices. However, the disparity between regions remains stark.

The data also illustrates alarming statistics in certain areas. Among the top five regions with the highest lifetime risk rates of maternal death are Chad (6.72%), the Central African Republic (5.35%), Nigeria (5.25%), South Sudan (5.09%), and Somalia (4.05%). These figures highlight severe challenges in maternal health services in these locations, often attributed to a lack of infrastructure, limited healthcare availability, and the social-political situation. For instance, Chad's and Nigeria's high rates reflect not only healthcare inadequacies but also contextual issues such as poverty, cultural attitudes toward women’s health, and overall governance of health systems.

Conversely, the bottom five areas identified—Belarus (0.0015%), Norway (0.0023%), Poland (0.0027%), Spain (0.0036%), and Malta (0.0040%)—exemplify the effectiveness of robust healthcare systems, with significant investments in maternal health. These countries benefit from advanced medical facilities, access to comprehensive prenatal and postnatal care, and effective governance that prioritizes women’s health. The stark contrast between these extremes underscores the urgent need to close the gap and ensure that all women, regardless of where they live, have access to the care they need.

Strategies to reduce the lifetime risk of maternal death include strengthening healthcare infrastructure, increasing access to skilled birth attendants, and improving education on reproductive health. Governments and health organizations must invest in expanding healthcare facilities, especially in remote or underserved areas. Training healthcare providers and ensuring that they have the necessary resources can help facilitate safer pregnancies and reduce complications associated with childbirth.

Furthermore, community engagement is essential in changing cultural perceptions and practices surrounding women’s health. Educational initiatives aimed at women can empower them to seek out necessary healthcare services and family planning resources. Public health campaigns that raise awareness about the importance of maternal health can also drive societal changes that favor the health and well-being of mothers.

However, it is crucial to note the flaws in the current measurement of the lifetime risk of maternal death. In some regions, the data collected may be unreliable due to reporting issues or the stigma associated with maternal mortality. Many deaths may go unrecorded, leading to underestimations of the actual lifetime risk. Additionally, focusing solely on statistics might neglect the qualitative aspects of care and the lived experiences of women. A more holistic approach, incorporating both quantitative data and qualitative feedback from women, would provide a clearer picture of maternal health dynamics.

In conclusion, while the global trend shows a decreasing lifetime risk of maternal death, significant disparities still exist between different regions. Addressing these disparities through targeted interventions, robust healthcare systems, and comprehensive educational initiatives is critical. The lifetime risk of maternal death percentage serves as an important marker for assessing maternal health and informs policy decisions aimed at reducing mortality rates 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 = 'SH.MMR.RISK.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.MMR.RISK.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))