Explore case detection and incidence rates in the WHO Tuberculosis data

explore_case_detection

# install.packages("getTBinR")
library(getTBinR)
# install.packages("tidyverse")
library(tidyverse)
# install.packages("gridExtra")
library(gridExtra)

# Get the data
tb_burden <- get_tb_burden(download_data = TRUE, save = TRUE)
dict <- get_data_dict(download_data = TRUE, save = TRUE)


# Look up variables with  dectection in their definition
search_data_dict(def = "detection")

# Map case detection in 2016
map_tb_burden(metric = "c_cdr", year = 2016)

# Map incidence rates in 2016
map_tb_burden(year = 2016)

# Get countries with lowest case detection rates
low_detect_countries <- tb_burden %>% 
  filter(year == 2016) %>% 
  group_by(country) %>% 
  summarise(c_cdr = min(c_cdr)) %>% 
  ungroup %>% 
  arrange(c_cdr) %>% 
  slice(1:20) %>% 
  pull(country) %>% 
  unique

#Overview of 20 countries with lowest case detection rates
p1 <- plot_tb_burden_overview(metric = "c_cdr",
                        countries = low_detect_countries) +
  theme(legend.position = "none")


# Overview of 20 countries with highest incidence rates
high_inc_countries <- tb_burden %>% 
  filter(year == 2016) %>% 
  group_by(country) %>% 
  summarise(e_inc_100k = max(e_inc_100k)) %>% 
  ungroup %>% 
  arrange(desc(e_inc_100k)) %>% 
  slice(1:20) %>% 
  pull(country) %>% 
  unique

p2 <- plot_tb_burden_overview(countries = high_inc_countries)

grid.arrange(p1, p2, ncol = 2, widths = c(0.46, 0.54))

## Countries with both highest incidence and lowest case detection rates
high_inc_low_detect <- intersect(low_detect_countries, high_inc_countries)

# Trends over time in cases detection rates in countries that are both highest 20 incidence and lowest 20 case detection rate
p3 <- plot_tb_burden(metric = "c_cdr",
               countries = high_inc_low_detect,
               facet = "country", scales = "fixed")
               
# Trends in incidence rates over time in countries that have both lowest case detection and highest incidence rates
p4 <- plot_tb_burden(countries = high_inc_low_detect,
                     facet = "country", scales = "free_y")

grid.arrange(p3, p4, ncol = 2)