Tuberculosis Report

In getTBinR: Access and Summarise World Health Organization Tuberculosis Data

knitr::opts_chunk$set(echo = FALSE,
                      warnings = FALSE,
                      eval = TRUE)

## Load the package
library(getTBinR)

## Load additional packages
library(ggplot2)

## Get the data
tb <- get_tb_burden(verbose = FALSE)

## Get the data dictionary
dict <- get_data_dict(verbose = FALSE)

##Assign parameters
country <- params$country
interactive <- params$interactive

TB incidence rates

inc_sum <- summarise_metric(tb, "e_inc_100k", country)

In r inc_sum$year r country had an estimated Tuberculosis incidence rate of r inc_sum$metric per 100,000 people making it number r inc_sum$world_rank in the world and number r inc_sum$region_rank regionally. In the last 10 years this has changed by r inc_sum$avg_change on average each year.

Regional and Global Trends Comparision

plot_tb_burden_summary(countries = country,
                       metric_label = "e_inc_100k",
                       compare_to_world = TRUE, 
                       compare_to_region = TRUE,
                       compare_all_regions = FALSE,
                       annual_change = FALSE,
                       facet = "Area",
                       scales = "free_y",
                       legend = "none",
                       interactive = interactive,
                       verbose = FALSE)

Rates Regional Breakdown

plot_tb_burden_overview(countries = country,
                        compare_to_region = TRUE,
                        interactive = interactive,
                        verbose = FALSE)

Case Detection Rates (CDR)

cdr_sum <- summarise_metric(tb, "c_cdr", country)

r country had an estimated case detection rate of r cdr_sum$metric% in r cdr_sum$year making it number r cdr_sum$world_rank in the world (with number 1 having the highest CDR) and number r cdr_sum$region_rank regionally. In the last 10 years this has changed by r cdr_sum$avg_change on average each year.

Regional Breakdown

plot_tb_burden_overview(metric = "c_cdr",
                        countries = country,
                        compare_to_region = TRUE,
                        interactive = interactive,
                        verbose = FALSE)

TB mortality rates - excluding HIV

mort_exc_hiv_sum <- summarise_metric(tb, "e_mort_exc_tbhiv_100k", country)

In r mort_exc_hiv_sum$year r country had an estimated Tuberculosis mortality rate (excluding HIV) of r mort_exc_hiv_sum$metric per 100,000 people making it number r mort_exc_hiv_sum$world_rank in the world and number r mort_exc_hiv_sum$region_rank regionally. In the last 10 years this has changed by r mort_exc_hiv_sum$avg_change on average each year.

Proportion of TB Cases that Died (excluding HIV) - Regional and Global Comparision

plot_tb_burden_summary(metric = "e_mort_exc_tbhiv_num",
                       denom = "e_inc_num",
                       rate_scale = 100,
                       countries = country,
                       compare_to_region = TRUE,
                       compare_all_regions = FALSE,
                       interactive = interactive,
                       verbose = FALSE,
                       facet = "Area",
                       scales = "free_y",
                       legend = "none") +
  labs(y = "Proportion (%) of TB cases that died (excluding HIV)")

Rates Regional Breakdown

plot_tb_burden_overview(metric = "e_mort_exc_tbhiv_100k",
                        countries = country,
                        compare_to_region = TRUE,
                        interactive = interactive,
                        verbose = FALSE)

TB HIV related mortality rates

mort_inc_hiv_sum <- summarise_metric(tb, "e_mort_tbhiv_100k", country)

In r mort_inc_hiv_sum$year r country had an estimated Tuberculosis mortality rate (related to HIV) of r mort_inc_hiv_sum$metric per 100,000 people making it number r mort_inc_hiv_sum$world_rank in the world and number r mort_inc_hiv_sum$region_rank regionally. In the last 10 years this has changed by r mort_inc_hiv_sum$avg_change on average each year.

Proportion of TB Cases that Died (related to HIV) - Regional and Global Comparision

plot_tb_burden_summary(metric = "e_mort_tbhiv_num",
                       denom = "e_inc_num",
                       rate_scale = 100,
                       countries = country,
                       compare_to_region = TRUE,
                       compare_all_regions = FALSE,
                       interactive = interactive,
                       verbose = FALSE,
                       facet = "Area",
                       scales = "free_y",
                       legend = "none") +
  labs(y = "Proportion (%) of TB cases that died (related to HIV)")

Rates Regional Breakdown

plot_tb_burden_overview(metric = "e_mort_tbhiv_100k",
                        countries = country,
                        compare_to_region = TRUE,
                        interactive = interactive,
                        verbose = FALSE)

getTBinR documentation built on July 2, 2020, 12:31 a.m.