Incidence of tuberculosis (per 100,000 people)

The incidence of tuberculosis (TB) is a crucial public health indicator that quantifies the number of new cases of TB diagnosed per 100,000 people within a specific population. This measurement serves as a key metric for assessing the burden of TB disease and tracking progress toward global health goals, particularly in minimizing infectious diseases. The significance of monitoring TB incidence lies in its ability to inform health policies, allocate resources effectively, and enhance awareness and education about TB management and prevention strategies.

As of 2023, the median value of TB incidence worldwide stands at 42.0 cases per 100,000 people. This figure, while indicative of global efforts to combat the disease, also highlights the disparities that exist between different regions and countries. Alarmingly, the top five areas with the highest TB incidence rates include the Marshall Islands at 692.0, Lesotho at 664.0, the Philippines at 643.0, Myanmar at 558.0, and the Central African Republic at 540.0. These figures starkly contrast with the bottom five areas, which report minimal to no cases: the British Virgin Islands and San Marino both at 0.0, Palestinian Territories at 0.35, Puerto Rico at 0.71, and the United Arab Emirates at 0.8. This disparity illustrates not only the differing healthcare capabilities and access across regions but also underscores the necessity for targeted interventions in high-burden areas.

Various factors contribute to the incidence of tuberculosis, including socioeconomic conditions, healthcare infrastructure, public health education, and access to effective treatment. Underlying issues such as poverty, malnutrition, and crowded living conditions create environments where TB can thrive. Additionally, the presence of co-morbidities, including HIV/AIDS, further exacerbates the risk of TB infection. Healthcare systems that are under-resourced or poorly managed may struggle to provide adequate screening, timely diagnosis, and effective treatment for those infected, leading to higher incidence rates. Moreover, stigma surrounding TB can deter individuals from seeking medical assistance, perpetuating the cycle of infection.

The relationship between TB incidence and other health indicators, such as mortality rates and the prevalence of HIV, is evident. For instance, areas with high rates of HIV often experience a correlated rise in TB incidence, as compromised immune systems increase vulnerability to infection. Moreover, the incidence of TB can also be tied to global events such as pandemics, which can disrupt health services and divert resources away from TB control efforts, as seen during the COVID-19 pandemic. This connection emphasizes the need for integrated healthcare approaches that address multiple health challenges simultaneously.

Effective strategies to reduce TB incidence involve comprehensive responses that address both the medical and socio-economic determinants of health. Strengthening healthcare infrastructures, enhancing community awareness, and promoting preventive healthcare measures, including vaccination and early screening, are essential steps. The World Health Organization (WHO) has established ambitious targets to reduce TB incidence as part of the End TB Strategy, which includes goals to increase the diagnosis and treatment of TB cases while also tackling drug-resistant TB strains.

Despite various strategies implemented globally, flaws remain in approaches to curb TB incidence. The uneven distribution of healthcare resources, limited public health funding, and bureaucratic inefficiencies can hinder progress toward eliminating tuberculosis. For instance, in regions where healthcare infrastructure is lacking, even the most well-designed interventions may fail to reach those in need. Additionally, the lack of political will or commitment can stall progress and leave vulnerable populations unprotected.

The world has seen a long-term decrease in TB incidence, dropping from 180.0 cases per 100,000 people in 2000 to 134.0 in 2023. However, this decline is slower than anticipated, given the ongoing challenges posed by new variants and the impact of global crises on health systems. Looking closely at this trend, it becomes increasingly clear that while progress is being made, the fight against tuberculosis is far from over. A collaborative and multifaceted approach is essential to sustain momentum and achieve elimination efforts globally. Sustained investment in healthcare systems is required not only to combat TB but also to build resilience against future healthcare challenges. As countries, organizations, and communities unify their efforts to tackle TB and its root causes, it is critical to ensure that no individual is left behind. Addressing TB effectively necessitates a commitment to long-term solutions that prioritize health equity, accessibility, and comprehensive healthcare education.

                    
# 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.INCD'

# 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.INCD'

# 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))