Country

Indicator

Value type

Values present

Tuberculosis treatment success rate (% of new cases)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	95	+2.15%	3
Angola	65	-2.99%	32
Albania	89	-1.11%	9
Andorra	100	0%	1
United Arab Emirates	87	+2.35%	11
Argentina	52	+8.33%	37
Armenia	84	-2.33%	14
Antigua & Barbuda	100	+33.3%	1
Australia	86	-2.27%	12
Austria	73	-2.67%	25
Azerbaijan	82	+1.23%	16
Burundi	94	-1.05%	4
Benin	91	+1.11%	7
Burkina Faso	82	0%	16
Bangladesh	95	-2.06%	3
Bahamas	87	+13%	11
Bosnia & Herzegovina	47	+2.17%	39
Belarus	87	+3.57%	11
Belize	75	-6.25%	23
Bolivia	79	-1.25%	19
Brazil	66	+1.54%	31
Barbados	100	+100%	1
Brunei	78	+11.4%	20
Bhutan	90	-4.26%	8
Botswana	76	0%	22
Central African Republic	90	+8.43%	8
Canada	74	-6.33%	24
Switzerland	65	-13.3%	32
Chile	71	-4.05%	27
China	95	+1.06%	3
Côte d’Ivoire	87	+2.35%	11
Cameroon	89	+1.14%	9
Congo - Kinshasa	95	0%	3
Congo - Brazzaville	82	+5.13%	16
Colombia	74	+1.37%	24
Comoros	85	-7.61%	13
Cape Verde	71	-22.8%	27
Cuba	85	+4.94%	13
Cayman Islands	100	0%	1
Cyprus	26	-49%	43
Czechia	63	-7.35%	34
Germany	62	-7.46%	35
Denmark	18	-10%	44
Dominican Republic	80	-2.44%	18
Algeria	90	0%	8
Ecuador	71	+2.9%	27
Egypt	87	-1.14%	11
Eritrea	94	+1.08%	4
Spain	74	+100%	24
Estonia	77	-8.33%	21
Ethiopia	86	-1.15%	12
Finland	1	-85.7%	45
Fiji	50	-10.7%	38
France	37	0%	42
Gabon	65	+14%	32
United Kingdom	84	-2.33%	14
Georgia	87	0%	11
Ghana	87	0%	11
Guinea	90	+1.12%	8
Gambia	87	+3.57%	11
Guinea-Bissau	78	+4%	20
Equatorial Guinea	82	+39%	16
Grenada	0	-100%	46
Guatemala	88	+1.15%	10
Guam	76	-3.8%	22
Guyana	70	+4.48%	28
Hong Kong SAR China	79	+6.76%	19
Honduras	86	+1.18%	12
Croatia	52	+13%	37
Haiti	85	+3.66%	13
Hungary	65	+1.56%	32
Indonesia	87	0%	11
India	89	+2.3%	9
Ireland	45	+1,400%	40
Iran	84	+1.2%	14
Iraq	94	+1.08%	4
Iceland	71	+24.6%	27
Israel	81	-5.81%	17
Jordan	82	-3.53%	16
Japan	65	+1.56%	32
Kazakhstan	90	+1.12%	8
Kenya	89	+2.3%	9
Kyrgyzstan	83	+2.47%	15
Cambodia	96	+1.05%	2
Kiribati	86	0%	12
South Korea	79	-1.25%	19
Kuwait	100	0%	1
Laos	88	+1.15%	10
Lebanon	85	-3.41%	13
Liberia	78	0%	20
Libya	60	-3.23%	36
St. Lucia	50	+100%	38
Sri Lanka	79	-1.25%	19
Lesotho	80	+3.9%	18
Lithuania	87	+1.16%	11
Macao SAR China	81	-1.22%	17
Morocco	87	0%	11
Moldova	85	+6.25%	13
Madagascar	75	-9.64%	23
Maldives	69	-10.4%	29
Mexico	70	-1.41%	28
Marshall Islands	83	-7.78%	15
North Macedonia	67	-15.2%	30
Mali	84	+2.44%	14
Myanmar (Burma)	88	+1.15%	10
Montenegro	93	-1.06%	5
Mongolia	87	-3.33%	11
Northern Mariana Islands	70	-30%	28
Mozambique	95	+1.06%	3
Mauritania	84	+1.2%	14
Mauritius	66	-18.5%	31
Malawi	90	0%	8
Malaysia	81	+2.53%	17
Namibia	88	+1.15%	10
New Caledonia	47	+95.8%	39
Niger	86	0%	12
Nigeria	93	+2.2%	5
Nicaragua	86	-1.15%	12
Norway	82	-5.75%	16
Nepal	93	+1.09%	5
Nauru	79	-14.1%	19
New Zealand	87	0%	11
Oman	44	-27.9%	41
Pakistan	95	+1.06%	3
Panama	74	-2.63%	24
Peru	84	-2.33%	14
Philippines	78	-2.5%	20
Palau	71	-29%	27
Papua New Guinea	73	0%	25
Puerto Rico	81	-1.22%	17
North Korea	87	-1.14%	11
Portugal	64	-5.88%	33
Paraguay	73	+8.96%	25
Palestinian Territories	86	-6.52%	12
French Polynesia	78	-14.3%	20
Qatar	77	+2.67%	21
Romania	84	+3.7%	14
Russia	67	+11.7%	30
Rwanda	89	+2.3%	9
Saudi Arabia	90	0%	8
Sudan	66	-25%	31
Senegal	90	+11.1%	8
Singapore	73	-6.41%	25
Solomon Islands	94	-2.08%	4
Sierra Leone	92	+1.1%	6
El Salvador	86	-2.27%	12
Somalia	91	-2.15%	7
Serbia	84	-1.18%	14
South Sudan	82	-2.38%	16
São Tomé & Príncipe	79	+12.9%	19
Suriname	73	+1.39%	25
Slovakia	89	+1.14%	9
Slovenia	72	-2.7%	26
Sweden	87	+2.35%	11
Eswatini	83	+5.06%	15
Sint Maarten	100	0%	1
Seychelles	50	+11.1%	38
Syria	93	-3.13%	5
Chad	83	+1.22%	15
Togo	86	-1.15%	12
Thailand	81	-4.71%	17
Tajikistan	92	0%	6
Timor-Leste	94	+2.17%	4
Tonga	100	0%	1
Trinidad & Tobago	71	-1.39%	27
Tunisia	93	+10.7%	5
Turkey	80	0%	18
Tuvalu	65	-2.99%	32
Tanzania	96	0%	2
Uganda	90	+2.27%	8
Ukraine	77	+2.67%	21
Uruguay	73	+1.39%	25
United States	72	-5.26%	26
Uzbekistan	88	-1.12%	10
St. Vincent & Grenadines	60	0%	36
Venezuela	83	-1.19%	15
Vietnam	89	-1.11%	9
Vanuatu	89	-1.11%	9
Yemen	90	+1.12%	8
South Africa	76	-3.8%	22
Zambia	93	+1.09%	5
Zimbabwe	89	-1.11%	9

Python
R

                    
# 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.TBS.CURE.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.TBS.CURE.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))

Compare

Tuberculosis treatment success rate (% of new cases)

Year: 2022

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