WHO Global Tuberculosis Report 2023

Code and data used to develop the WHO Global Tuberculosis Report for 2023. The report was published on 7 November 2023 at https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2023/

Folders

data: External datasets:
- bcg: BCG coverage indicator from the WHO Global Health Observatory
- gtb: R binary datasets extracted from the WHO gobal TB database:
  - other: Country lists, population estimates, reference lists, survey results and external indicators
  - snapshot_yyyy-mm-dd: Snapshot of country-reported data
- ihme: VR data from IHME
- mortality: VR data from the WHO Mortality database
- unaids: HIV estimates from UNAIDS
disaggregation: Pete Dodd's R scripts to estimate incidence and mortality disaggregated by age group and sex.
doc: A PDF version of the web-based data collection form used by countries to report data to WHO. The PDF also shows database variable names.
drtb: Pete Dodd's R and Stan scripts to estimate drug-resistant TB burden.
dynamic: Nim Arinaminpathy's Matlab code for dynamic modelling of TB incidence and mortality for the period 2020-2022 in selected countries.
finance: Andrew Siroka and Peter Nguhiu's R and Stata code for analysing TB financing data.
import: R scripts to load saved data files from the WHO gobal TB database and the Global Health Observatory.
inc_mort: Mathieu Bastard and Philippe Glaziou's R scripts to produce estimates of TB incidence and mortality
lives_saved: Takuya Yamanaka and Philippe Glaziou's R scripts to produce estimates of the number of death averted by TB treatment and ART since 2005.
report: R Markdown scripts by Mathieu Bastard/Philippe Glaziou, Irwin Law, Peter Nguhiu, Hazim Timimi and Takuya Yamanaka to generate tables, static figures, interactive Kendo UI charts and text for the 2023 edition of the WHO Global Tuberculosis Report web pages and the report PDF.

Global tuberculosis database data sets

The following sections show the data object names chosen in previous years. If you use the load_gtb() function you don't need to know if a data object is part of a snapshot or other, not does your code have to use the same object name.

For example, the following line loads the most recently saved snapshot of notifications into a dataframe / data table called notifs:

notifs <- load_gtb("tb")

Snapshot data

agg: TB/HIV indicators with rules to be used to calculate aggregates from view_TME_master_TBHIV_for_aggregates
covid: Impact of COVID on services and response to the UNHLM commitments from view_TME_master_covid_unhlm
drfq: DRS records used to estimate fluoroquinolone resistance among RR-TB patients from view_DRS_for_estimation_sldst
drhnew: DRS records used to estimate HR-TB among new TB patients from view_DRS_for_estimation_new_INH
drhret: DRS records used to estimate HR-TB among previously treated TB patients from view_DRS_for_estimation_ret_INH
drnew: DRS records used to estimate RR-TB among new TB patients from view_DRS_for_estimation_new
drret: DRS records used to estimate RR-TB among previously treated TB patients from view_DRS_for_estimation_ret
drroutine: Routine drug resistance surveillance records from view_TME_master_dr_surveillance
ltbi: Estimates of TPT coverage among children (numbers derived from reported data) from view_TME_estimates_ltbi
monthly: Provisional monthly or quarterly notifications from dcf.latest_provisional_c_newinc
sty: Services, PPM, community engagement, M&E systems from view_TME_master_strategy
tb: TB notifications from view_TME_master_notifications
tpt: TB preventive treatment from view_TME_master_contacts_tpt
tx: Treatment outcomes from view_TME_master_outcomes
vrgtb: VR data reported by countries in the European Region from dcf.latest_vr

Other data

Explanatory

dic: Data dictionary from view_TME_data_dictionary
codes: Meaning of codes used for categorical variables from view_TME_data_codes

Reference: countries, country groups, population and SDG indicators

cty: Country and area names in 4 languages, their codes and WHO region and status from view_TME_master_report_country
datacoll: Options set for the data collection form for each country-data collection year combinations from view_TME_master_data_collection
grptypes: Themes by which to group countries from view_country_group_types
grp: Country groups within each grouping theme (e.g. the 4 income groups of High, Upper Middle, Lower Middle and Low in the World Bank income classification) from view_country_groups
grpmbr: Countries belonging to each country group from view_country_group_membership
pop: UN Population Division population estimates from view_TME_estimates_population
sdg: SDG indicator data and codes relevant to TB incidence from external_indicators.view_indicator_data
sdgdef: Full names of SDG indicators and their sources from "external_indicators.view_indicator_definition

Surveys and survey results

svy.cc: Results of catastrophic costs surveys from survey.view_catastrophic_costs_survey
svy.prev: Prevalence estimates resulting from prevalence surveys from survey.view_prevalence_survey_estimates
svy.prevcases: Numbers of TB cases found in prevalence surveys from survey.view_prevalence_survey_cases
svy.prevchar: Characteristics of prevalence surveys from survey.view_prevalence_survey

Codes used in survey records:

svy.agegr: Codes for age groups from survey.age_group
svy.areatype: Codes for area types from survey.area_type
svy.casetype: Codes for case types from survey.case_type
svy.patientgr: Codes for patient groups from survey.patient_group
svy.screen: Codes for screening methods from survey.screen_group
svy.sex: Codes for sex from survey.sex

GTB-TME/gtbreport2023