Lifetime risk of maternal death (1 in: rate varies by country)

The lifetime risk of maternal death is a critical health indicator that signifies the probability that a woman will die from maternal causes, often defined as complications arising from pregnancy and childbirth, during her lifetime. This statistic is typically expressed as "1 in X" and varies greatly from one country to another. Understanding this indicator is essential as it reflects the overall health care systems, access to medical services, socio-economic conditions, and educational opportunities available to women in different regions.

This metric plays a pivotal role in assessing the effectiveness of maternal health programs and policies worldwide. It offers insights into the quality of healthcare services provided to women, particularly during pregnancy and childbirth. The maternal death ratio can be an indicator of broader health system efficiencies and societal values regarding women's rights and health. Monitoring changes in the lifetime risk of maternal death can inform public health strategies, track progress towards global health goals, and identify areas that need more intensive intervention.

The importance of the lifetime risk of maternal death becomes even clearer when considered in relation to other key health indicators. For instance, neonatal mortality rates, household health expenditure, women's education levels, and general health system performance all correlate closely with maternal mortality rates. Regions with lower educational attainment and inadequate healthcare infrastructure often report higher risks of maternal death. Access to family planning services and prenatal care has also been shown to significantly reduce the risks associated with childbirth.

Several factors influence the lifetime risk of maternal death. These include socioeconomic status, which affects access to quality healthcare; cultural practices that may discourage seeking medical help; available maternal and reproductive health education; and systemic flaws within healthcare infrastructure, including insufficient healthcare workers and inadequate facilities. Political instability and conflict can further exacerbate these issues, leading to a dramatic increase in maternal mortality rates. The disparities in maternal health are stark when comparing developed and developing countries, often leading to a life-threatening situation for mothers in less privileged nations.

Analyzing the data from 2020 reveals that the median value for the lifetime risk of maternal death globally was significantly high at 850.0, illustrating the continued challenge faced by many regions. Among the top areas with the highest risk, Belarus, Norway, Poland, Spain, and Malta, all exhibit an alarming trend that requires immediate attention and corrective actions. Belarus stands out with a staggering figure of 65,000.0, followed by Norway at 43,000.0, Poland at 37,000.0, Spain at 28,000.0, and Malta at 25,000.0. This data indicates a critical susceptibility within these healthcare systems, prompting a pressing need for comprehensive maternal health policies. On the other hand, countries like Chad (15.0), Central African Republic (19.0), Nigeria (19.0), South Sudan (20.0), and Somalia (25.0) exhibit some of the lowest lifetime risks, providing an interesting contrast that illustrates how improved healthcare access and maternal education can drastically change outcomes.

Global trends over the last two decades show a gradual improvement in maternal health. From a lifetime risk of maternal death of 120 in the year 2000, the global average increased marginally, reaching 210 in 2020. This shows a slight increase, which, while troubling, underscores ongoing efforts to improve. However, there is still a long way to go to reach a more acceptable level, particularly in regions that continuously grapple with healthcare access issues and lack sufficient resources. For instance, between 2000 and 2016, the worldwide lifetime risk of maternal death varied between 170 and 190, highlighting periods of stagnation in progress, followed by a marginal increase up to 210 by 2020.

To mitigate these risks, various strategies can be employed by countries, including increasing investment in healthcare infrastructure, training more healthcare professionals, improving access to obstetric care, and implementing robust health education programs that empower women. Solutions could involve community-based healthcare initiatives that focus on maternal care and preventative education. Ensuring that women have access to family planning, prenatal care, and skilled birth attendants can dramatically reduce the risk of complications associated with childbirth.

Despite the recognition of the importance of maternal care, flaws remain in how data is collected and reported, which can lead to underestimations of actual risks in some areas. In many regions, particularly those affected by conflict or economic instability, the lack of reliable data collection methods impedes the ability to formulate effective responses to the challenges posed by maternal health. Furthermore, societal attitudes toward gender roles and women's rights can complicate efforts to improve maternal healthcare, leading to resistance from communities accustomed to traditional practices. Addressing these deeply rooted cultural factors is as crucial as healthcare interventions themselves.

The lifetime risk of maternal death is a vital indicator of women's health and rights worldwide, emphasizing the urgent need for focused global health strategies. By understanding its intricacies and relationships to other health measures, stakeholders can devise effective policies to enhance maternal healthcare, thus ensuring safer pregnancies, higher survival rates, and improved health outcomes for women across the globe.

                    
# 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'

# 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'

# 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))