UHC service coverage index

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 41 -2.38% 49
Angola Angola 37 -5.13% 52
Albania Albania 64 0% 27
Andorra Andorra 79 +1.28% 12
United Arab Emirates United Arab Emirates 82 +9.33% 9
Argentina Argentina 79 +1.28% 12
Armenia Armenia 68 -2.86% 23
Antigua & Barbuda Antigua & Barbuda 76 +1.33% 15
Australia Australia 87 0% 4
Austria Austria 85 +1.19% 6
Azerbaijan Azerbaijan 66 -1.49% 25
Burundi Burundi 41 -2.38% 49
Belgium Belgium 86 -1.15% 5
Benin Benin 38 +8.57% 51
Burkina Faso Burkina Faso 40 +5.26% 50
Bangladesh Bangladesh 52 +4% 39
Bulgaria Bulgaria 73 -3.95% 18
Bahrain Bahrain 76 0% 15
Bahamas Bahamas 77 -2.53% 14
Bosnia & Herzegovina Bosnia & Herzegovina 66 -1.49% 25
Belarus Belarus 79 -2.47% 12
Belize Belize 68 -1.45% 23
Bolivia Bolivia 65 0% 26
Brazil Brazil 80 -1.23% 11
Barbados Barbados 77 -2.53% 14
Brunei Brunei 78 +1.3% 13
Bhutan Bhutan 60 0% 31
Botswana Botswana 55 0% 36
Central African Republic Central African Republic 32 +3.23% 55
Canada Canada 91 0% 1
Switzerland Switzerland 86 0% 5
Chile Chile 82 0% 9
China China 81 0% 10
Côte d’Ivoire Côte d’Ivoire 43 +4.88% 47
Cameroon Cameroon 44 +2.33% 46
Congo - Kinshasa Congo - Kinshasa 42 +5% 48
Congo - Brazzaville Congo - Brazzaville 41 +5.13% 49
Colombia Colombia 80 0% 11
Comoros Comoros 48 +4.35% 42
Cape Verde Cape Verde 71 +2.9% 20
Costa Rica Costa Rica 81 -1.22% 10
Cuba Cuba 83 0% 8
Cyprus Cyprus 81 0% 10
Czechia Czechia 84 +1.2% 7
Germany Germany 88 0% 3
Djibouti Djibouti 44 -2.22% 46
Dominica Dominica 49 -31.9% 41
Denmark Denmark 82 0% 9
Dominican Republic Dominican Republic 77 +2.67% 14
Algeria Algeria 74 0% 17
Ecuador Ecuador 77 -2.53% 14
Egypt Egypt 70 0% 21
Eritrea Eritrea 45 -2.17% 45
Spain Spain 85 0% 6
Estonia Estonia 79 0% 12
Ethiopia Ethiopia 35 -2.78% 53
Finland Finland 86 +1.18% 5
Fiji Fiji 58 -1.69% 33
France France 85 +1.19% 6
Micronesia (Federated States of) Micronesia (Federated States of) 48 +4.35% 42
Gabon Gabon 49 0% 41
United Kingdom United Kingdom 88 +1.15% 3
Georgia Georgia 68 -1.45% 23
Ghana Ghana 48 +4.35% 42
Guinea Guinea 40 +2.56% 50
Gambia Gambia 46 0% 44
Guinea-Bissau Guinea-Bissau 37 +2.78% 52
Equatorial Guinea Equatorial Guinea 46 +4.55% 44
Greece Greece 77 -2.53% 14
Grenada Grenada 70 -5.41% 21
Guatemala Guatemala 59 -3.28% 32
Guyana Guyana 76 -1.3% 15
Honduras Honduras 64 -3.03% 27
Croatia Croatia 80 0% 11
Haiti Haiti 54 +1.89% 37
Hungary Hungary 79 0% 12
Indonesia Indonesia 55 -1.79% 36
India India 63 -1.56% 28
Ireland Ireland 83 +1.22% 8
Iran Iran 74 -1.33% 17
Iraq Iraq 59 +1.72% 32
Iceland Iceland 89 0% 2
Israel Israel 85 0% 6
Italy Italy 84 -1.18% 7
Jamaica Jamaica 74 -3.9% 17
Jordan Jordan 65 -2.99% 26
Japan Japan 83 0% 8
Kazakhstan Kazakhstan 80 -2.44% 11
Kenya Kenya 53 +3.92% 38
Kyrgyzstan Kyrgyzstan 69 -2.82% 22
Cambodia Cambodia 58 0% 33
Kiribati Kiribati 48 0% 42
St. Kitts & Nevis St. Kitts & Nevis 79 +2.6% 12
South Korea South Korea 89 0% 2
Kuwait Kuwait 78 +1.3% 13
Laos Laos 52 +1.96% 39
Lebanon Lebanon 73 -1.35% 18
Liberia Liberia 45 +4.65% 45
Libya Libya 62 -3.13% 29
St. Lucia St. Lucia 77 0% 14
Sri Lanka Sri Lanka 67 +1.52% 24
Lesotho Lesotho 53 0% 38
Lithuania Lithuania 75 0% 16
Luxembourg Luxembourg 83 -3.49% 8
Latvia Latvia 75 -1.32% 16
Morocco Morocco 69 +1.47% 22
Monaco Monaco 86 0% 5
Moldova Moldova 71 -1.39% 20
Madagascar Madagascar 35 +6.06% 53
Maldives Maldives 61 -10.3% 30
Mexico Mexico 75 +1.35% 16
Marshall Islands Marshall Islands 59 -3.28% 32
North Macedonia North Macedonia 74 0% 17
Mali Mali 41 +2.5% 49
Malta Malta 85 +2.41% 6
Myanmar (Burma) Myanmar (Burma) 52 -13.3% 39
Montenegro Montenegro 72 0% 19
Mongolia Mongolia 65 -2.99% 26
Mozambique Mozambique 44 +2.33% 46
Mauritania Mauritania 40 +11.1% 50
Mauritius Mauritius 66 -2.94% 25
Malawi Malawi 48 0% 42
Malaysia Malaysia 76 -2.56% 15
Namibia Namibia 63 +1.61% 28
Niger Niger 35 +2.94% 53
Nigeria Nigeria 38 -11.6% 51
Nicaragua Nicaragua 70 -4.11% 21
Netherlands Netherlands 85 0% 6
Norway Norway 87 +1.16% 4
Nepal Nepal 54 +8% 37
Nauru Nauru 60 0% 31
New Zealand New Zealand 85 0% 6
Oman Oman 70 0% 21
Pakistan Pakistan 45 +2.27% 45
Panama Panama 78 -1.27% 13
Peru Peru 71 -5.33% 20
Philippines Philippines 58 -3.33% 33
Palau Palau 65 -1.52% 26
Papua New Guinea Papua New Guinea 30 0% 56
Poland Poland 82 0% 9
North Korea North Korea 68 -5.56% 23
Portugal Portugal 88 +1.15% 3
Paraguay Paraguay 72 -2.7% 19
Qatar Qatar 76 +1.33% 15
Romania Romania 78 -1.27% 13
Russia Russia 79 0% 12
Rwanda Rwanda 49 +4.26% 41
Saudi Arabia Saudi Arabia 74 +2.78% 17
Sudan Sudan 44 -2.22% 46
Senegal Senegal 50 -1.96% 40
Singapore Singapore 89 +1.14% 2
Solomon Islands Solomon Islands 47 +2.17% 43
Sierra Leone Sierra Leone 41 +7.89% 49
El Salvador El Salvador 78 0% 13
San Marino San Marino 77 0% 14
Somalia Somalia 27 +3.85% 58
Serbia Serbia 72 -6.49% 19
South Sudan South Sudan 34 +9.68% 54
São Tomé & Príncipe São Tomé & Príncipe 59 -1.67% 32
Suriname Suriname 63 -12.5% 28
Slovakia Slovakia 82 0% 9
Slovenia Slovenia 84 -1.18% 7
Sweden Sweden 85 0% 6
Eswatini Eswatini 56 -3.45% 35
Seychelles Seychelles 75 +1.35% 16
Syria Syria 64 +3.23% 27
Chad Chad 29 +7.41% 57
Togo Togo 44 +7.32% 46
Thailand Thailand 82 0% 9
Tajikistan Tajikistan 67 -4.29% 24
Turkmenistan Turkmenistan 75 +1.35% 16
Timor-Leste Timor-Leste 52 +4% 39
Tonga Tonga 57 0% 34
Trinidad & Tobago Trinidad & Tobago 75 -1.32% 16
Tunisia Tunisia 67 -1.47% 24
Turkey Turkey 76 -1.3% 15
Tuvalu Tuvalu 52 0% 39
Tanzania Tanzania 43 +2.38% 47
Uganda Uganda 49 +2.08% 41
Ukraine Ukraine 76 -1.3% 15
Uruguay Uruguay 82 -1.2% 9
United States United States 86 +1.18% 5
Uzbekistan Uzbekistan 75 0% 16
St. Vincent & Grenadines St. Vincent & Grenadines 69 -9.21% 22
Venezuela Venezuela 75 +1.35% 16
Vietnam Vietnam 68 -1.45% 23
Vanuatu Vanuatu 47 +2.17% 43
Samoa Samoa 55 0% 36
Yemen Yemen 42 0% 48
South Africa South Africa 71 0% 20
Zambia Zambia 56 +3.7% 35
Zimbabwe Zimbabwe 55 0% 36

                    
# 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.UHC.SRVS.CV.XD'

# 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.UHC.SRVS.CV.XD'

# 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))