Exploring HIV and TB incidence in high burden countries

HIV_and_TB

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

# Get the data - WHO data
tb_df <- get_tb_burden()
dict <- get_data_dict()

# Look up variables with mortality in their definition
search_data_dict(def = "incidence")

# Map incidence rates in 2016
map_tb_burden(tb_df, dict, metric = "e_inc_100k", year = 2016)

ggsave("map_inc_2016_TB.png", width = 8, height = 4, dpi = 330)

 # Get countries with highest incidence rates in the data
high_inc_countries <- tb_df %>% 
  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

#Find Proportion of cases that have HIV
search_data_dict(def = "HIV")

#Overview of 20 countries with highest percentages of HIV in TB cases
map_tb_burden(tb_df, dict, metric = "e_tbhiv_prct", year = 2016)

ggsave("map_HIV_per_in_TB.png", width = 8, height = 4, dpi = 330)

## Get countries with highest percentage of HIV in TB
high_HIV_in_TB_countries <- tb_df %>% 
  filter(year == 2016) %>% 
  group_by(country) %>% 
  summarise(e_tbhiv_prct = max(e_tbhiv_prct)) %>% 
  ungroup %>% 
  arrange(desc(e_tbhiv_prct)) %>% 
  slice(1:20) %>% 
  pull(country) %>% 
  unique

## Intersection between high incidence and HIV
high_inc_high_HIV_countries <- intersect(high_inc_countries, high_HIV_in_TB_countries)
high_inc_high_HIV_countries

## Trend overview in high inc and high HIV countries - TB incidence rates  
p1 <- plot_tb_burden_overview(tb_df, dict, metric = "e_inc_100k", 
                              countries = high_inc_high_HIV_countries) +
  theme(legend.position = "none")

## Trend overview in high inc and high HIV countries - Percentage of cases with HIV 
p2 <- plot_tb_burden_overview(tb_df, dict, metric = "e_tbhiv_prct", 
                              countries = high_inc_high_HIV_countries)

cp1 <- grid.arrange(p1, p2, ncol = 2, widths = c(0.46, 0.54))
cp1

ggsave("overview_high_inc_high_HIV_per.png", cp1,  width = 16, height = 8, dpi = 330)
## Trend overview in high inc and high HIV countries - TB incidence rates  
p3 <- plot_tb_burden(tb_df, dict, metric = "e_inc_100k", 
                     countries = high_inc_high_HIV_countries,
                     facet = "country", scales = "free_y")

## Trends in high inc and high HIV countries - Percentage of cases with HIV 
p4 <- plot_tb_burden(tb_df, dict, metric = "e_tbhiv_prct", 
                     countries = high_inc_high_HIV_countries,
                     facet = "country", scales = "free_y")

cp2 <- grid.arrange(p3, p4, ncol = 2)
cp2

ggsave("trends_high_inc_high_HIV_per.png", cp2, width = 16, height = 8, dpi = 330)