Success Story: Modeling TB Epidemiology and Control Efforts in California

Project highlights

• Each year, tuberculosis (TB) causes approximately 500 deaths in the U.S. and results in over $500 million in TB disease costs.

• The rate of decline in tuberculosis incidence in recent years is estimated to be too slow to reach the national goal of tuberculosis elimination within this century. To change this, tuberculosis prevention and control needs innovative approaches.

• Over 70% of reported tuberculosis cases occur among non-U.S.–born persons. CDC attributes over 80% of reported tuberculosis cases to non-recent transmission (i.e. activation of latent tuberculosis infection).

• Three mathematical models of tuberculosis transmission were compared to inform tuberculosis control efforts. All three focused on data inputs from California, which accounted for 23% of US tuberculosis cases in 2018.

• While these models focused on California, key programmatic scenarios may be applicable to other areas of the U.S. after accounting for local resources, epidemiologic patterns, and public health priorities.

Background

The U.S. reported its lowest tuberculosis (TB) incidence―the number of new TB cases―in 2018: 2.8 cases per 100,000 persons.1 However, the decline in TB incidence has slowed in recent years.12 The current pace of this decline is estimated3 to be too slow to reach the national goal of TB elimination (defined as an annual incidence of less than one case per 1 million population)45 within this century.

In recent years, TB has caused approximately 500 deaths annually in the U.S. and resulted in over US $500 million in TB disease treatment and societal costs of premature death.6 Reducing these numbers requires new approaches for TB prevention and control.

Recent modeling efforts4 suggest that scaling up existing TB prevention and control efforts may lead to substantial progress in reducing U.S. TB cases, death, and costs.

Approach

Recommendations for strengthening TB control efforts could be found by comparing three mathematical models of TB transmission and identifying supporting evidence to support policies. This will enhance existing interventions to prevent TB.

The models were developed separately by academic institutions at Harvard University, Johns Hopkins University, and the University of California at San Francisco, in partnership with CDC scientists. All three models used the same data inputs from California,4 which accounted for 23% of US TB cases in 2018.1

The models included in this comparison varied in terms of their key structural features, such as:

• Modeling approach: deterministic models (the same inputs always produce the same outputs) versus stochastic models (the same inputs produce a range of outputs due to randomness).
• Age structure: all ages versus age 15 years and older; stratified by single year of age or stratified into several age bands.
• Stratification of non-U.S.-born residents: years in the U.S. and/or country of origin.
• Risk factors for TB exposure and progression: list of included risk factors; number of risk factors.
• Other population stratifications: prior LTBI/TB treatment alone or in combination with TB drug resistance.

Results

Each model projected TB incidence, TB deaths, and incident (newly occurring) and prevalent (existing) latent TB infection (LTBI) from 2020 to 2050 under several standardized scenarios. The base-case scenario estimated the effect of continued current population coverage and treatment services for TB and LTBI. This includes routine contact investigation of persons exposed to infectious TB patients.

Under the base-case, each model confirmed findings of an earlier analysis using national data.3 Without a major scale-up of TB interventions or changes in major TB determinants, TB incidence and deaths will continue to decline slowly and remain well above the elimination goal through 2050.

The remaining two scenarios modeled the effect of:

1. Ensuring no TB or LTBI among new California residents from 2018 onwards
2. Targeted one-time testing and treatment for LTBI for 25% of the non-U.S.-born population, conducted in 2018

Despite their differences, all models agreed on the scenarios having potential to further TB elimination. Eliminating TB and LTBI among new California residents (scenario 1) consistently had the highest predicted impact, leading to an estimated decrease of 21.9% and 38.1% in cumulative TB cases. It highlighted the importance of strengthening and expanding testing and treatment of LTBI programs.

For the scenario that scaled up one-time LTBI testing and treatment for 25% of all non-U.S.-born residents of California (scenario 2), models predicted a relatively stable 4.6% to 13.4% reduction in TB cases. It demonstrated that even short-term interventions to identify and treat LTBI might create ongoing benefits for reducing TB cases and deaths.

Across examined scenarios, model agreement was strongest when there were rigorous empirical data available to parameterize and calibrate models (e.g., TB case notifications and deaths). This highlighted the importance of the high-quality, up-to-date data for mathematical modeling.

Lessons learned

These findings highlight that strategies to achieve TB elimination goals will need to incentivize providers to test and treat LTBI among non-U.S.-born populations. There must also be greater access to LTBI diagnosis and treatment for non-U.S.-born individuals, such as access to healthcare services upon arrival in the United States.57

Although the models used in this analysis were based on TB epidemiology in California, the conclusions about programmatic interventions with the greatest potential impact were similar to those previously analyzed on a national level.

These findings may be applicable to other parts of the United States. Similar modeling at the jurisdiction level can account for the area-specific nuances of TB epidemiology. It may also help adjust and prioritize interventions and estimating local resources needed to achieve TB elimination goals.2