Methodology

How We Calculate TB Funding Impact

This page explains the methodology used to estimate the impact of TB funding reductions on mortality. Our approach is based on published research, epidemiological models, and data from USAID, WHO, and other global health organizations.

Data Sources

Our estimates are based on the following data sources:

• World Health Organization (WHO) Global TB Reports
• USAID TB program data and reports
• Peer-reviewed research on TB mortality and treatment efficacy
• Epidemiological models of TB transmission and mortality
• Country-specific TB prevalence and mortality data

Calculation Methodology

The established TIME (TB Impact Model and Estimates) model was used to generate estimates of the funding freeze. To analyse the potential impact, 26 high-burden TB countries were categorized based on their dependency on U.S. funding, which we estimated from expenditure data reported to WHO:

• Low dependency (0%–22%)
• Moderate dependency (23%–37%)
• High dependency (>37%)

Then, one representative country was selected from each category—Country 1 (low dependency), Country 2 (moderate dependency), and Country 3 (high dependency) (country names anonymized). By calibrating country-specific TB models with epidemiological data, the impact of various disruption scenarios was assessed:

• S1- recovery to baseline service coverage within three months
• S2- recovery to baseline service coverage within one year
• S3 -failure to recover to baseline service coverage.

Using a simple extrapolation method, the global implications of this funding freeze was estimated.

Model Uncertainty

In the displayed counters, to ensure simplicity of communication, uncertainty in the estimates was not included. For those who are interested in an understanding of the uncertainty in the estimates: Scenario 1, the best-case scenario, the total 1-year impact is expected to be an additional 28,936 [21,183-34,265] deaths and 51,458 [38,719-61,014] new TB cases. For Scenario 2, where we assume one year to return to baseline coverage, the total 1-year impact is expected to be an additional 55,342 [40,026-65,430] deaths and 79,014 [59,321-93,973] new TB cases. Finally, for Scenario 3, the worst-case scenario where we assume the disruption will persist into the future, the total 1-year impact is expected to be an additional 71,348 [51,245-84,492] deaths and 92,279 [69,141-109,954] new TB cases. To generate uncertainty bounds, we varied the country level on-the-ground dependency on USAID.

Impact Over Five Years

The impact of the funding freeze over longer time periods is even more pronounced. Over five years, for Scenario 1- the expected impact of the 90-day disruption followed by an immediate return to baseline coverage results in 99,860 [69,911-124,944] additional deaths and 634,736 [466,629-784,174] additional TB cases between now and 2030 (Scenario 1). For Scenario 2, a delayed return to baseline coverage of one year- the total impact through 2030 is 268,579 [185,756-337,875] additional deaths, and 1,660,036 [1,210,411-2,060,012] additional TB cases. Finally, for Scenario 3, the worst-case scenario where we assume the disruption will persist into the future, the total 5-year impact is expected to be an additional 2,243,717 [1,466,894-2,923,105] deaths and 10,676,456 [7,530,921-13,558,385] new TB cases

Review of the Estimates

Assumptions and results have been reviewed by: Carel Pretorius (Avenir Health), Sandip Mandal (Avenir Health) and the Stop TB Partnership

Updates and Revisions

We regularly update our methodology as new data becomes available. Major revisions to our methodology are documented here, with dates of implementation.