Overview
In this vignette the global distribution of zoonotic TB and variables thought to be related to zoonotic TB is explored using global maps. This includes TB incidence rates, the proportion of TB cases that are extra-pulmonary, the proportion of TB cases that are HIV positive, the presence of zoonotic TB in domesticated/wild animal populations, and the proportion of the population that is rural. Note that conclusions drawn from these maps require support from other forms of analysis as maps can be difficult to interpret (due to country size inducing visual bias).
Data
Use the latest available data for each variable. Note that this may introduce some bias - as TB incidence rates etc. have been changing over time. See the data exploration vignette for further details on data structure.
latest_data <- link_data(verbose = FALSE) %>%
get_latest_combined_data()
latest_data
#> # A tibble: 244 x 30
#> country country_code g_whoregion tb_year tb_cases tb_inc tb_inc_lo
#> <chr> <fct> <fct> <dbl> <int> <dbl> <dbl>
#> 1 Afghan… AFG Eastern Me… 2018 70000 189 122
#> 2 Albania ALB Europe 2018 510 18 15
#> 3 Algeria DZA Africa 2018 29000 69 53
#> 4 Americ… ASM Western Pa… 2018 0 0 0
#> 5 Andorra AND Europe 2018 2 3 2.6
#> 6 Angola AGO Africa 2018 109000 355 230
#> 7 Anguil… AIA Americas 2018 3 22 14
#> 8 Antigu… ATG Americas 2018 6 6 5.1
#> 9 Argent… ARG Americas 2018 12000 27 23
#> 10 Armenia ARM Europe 2018 920 31 24
#> # … with 234 more rows, and 23 more variables: tb_inc_hi <dbl>,
#> # prop_tb_ep <dbl>, prop_hiv <dbl>, prop_hiv_lo <dbl>,
#> # prop_hiv_hi <dbl>, z_tb_year <dbl>, z_tb_id <int>,
#> # z_tb_geo_coverage <fct>, z_tb_study_pop <fct>,
#> # z_tb_multi_year_study <fct>, tb_z_prop <dbl>, tb_z_prop_lo <dbl>,
#> # tb_z_prop_hi <dbl>, tb_z_prop_se <dbl>, z_tb_animal_year <dbl>,
#> # z_tb_dom_animal <fct>, z_tb_wild_animal <fct>, demo_year <dbl>,
#> # population <dbl>, prop_rural <dbl>, animal_year <dbl>, cattle <int>,
#> # cattle_per_head <dbl>Proportion of tuberculosis (TB) that is zoonotic
prop_map <- function(df) {
map <- global_map(df, variable = "tb_z_prop",
variable_label = "% of TB cases that are zoonotic",
trans = "sqrt", fill_labels = scales::percent)
return(map)
}
prop_map(latest_data)
TB incidence rates
Incidence rate data is from 2017 for consitancy.
link_data(verbose = FALSE) %>%
filter(year == 2017) %>%
global_map(variable = "tb_inc",
variable_label = "TB incidence rate (per 100,000)",
trans = "sqrt")
Proportion of TB cases that are extra-pulmonary only
Data is again from 2017 only.
link_data(verbose = FALSE) %>%
filter(year == 2017) %>%
global_map(variable = "prop_tb_ep",
variable_label = "% of TB cases that are extra-pulmonary only",
fill_labels = scales::percent)
Proportion of TB cases that are HIV positive
Data is again from 2017 only.
link_data(verbose = FALSE) %>%
filter(year == 2017) %>%
global_map(variable = "prop_hiv",
variable_label = "% of TB cases that are HIV positive",
trans = "sqrt",
fill_labels = scales::percent)
Presence of zoonotic TB in animals
Data is latest from 2018 only.
Proportion of the population that is rural.
latest_data %>%
global_map(variable = "prop_rural",
variable_label = "% of population that is rural",
fill_labels = scales::percent)
