Modeling the Impact of Recommendations for Primary Care–Based Screening for Latent Tuberculosis Infection in California

Abstract

Objective Targeted testing and treatment of persons with latent tuberculosis infection (LTBI) is a critical component of the US tuberculosis (TB) elimination strategy. In January 2016, the California Department of Public Health issued a tool and user guide for TB risk assessment (California tool) and guidance for LTBI testing, and in September 2016, the US Preventive Services Task Force (USPSTF) issued recommendations for LTBI testing in primary care settings. We estimated the epidemiologic effect of adherence to both recommendations in California. Methods We used an individual-based Markov micro-simulation model to estimate the number of cases of TB disease expected through 2026 with baseline LTBI strategies compared with implementation of the USPSTF or California tool guidance. We estimated the risk of LTBI by age and country of origin, the probability of being in a targeted population, and the probability of presenting for primary care based on available data. We assumed 100% adherence to testing guidance but imperfect adherence to treatment. Results Implementation of USPSTF and California tool guidance would result in nearly identical numbers of tests administered and cases of TB disease prevented. Perfect adherence to either recommendation would result in approximately 7000 cases of TB disease averted (40% reduction compared with baseline) by 2026. Almost all of this decline would be driven by a reduction in the number of cases among non–US-born persons. Conclusions By focusing on the non–US-born population, adherence to LTBI testing strategies recommended by the USPSTF and the California tool could substantially reduce the burden of TB disease in California in the next decade.

State-level prevalence estimates of latent tuberculosis infection in the United States by medical risk factors, demographic characteristics and nativity

“…Additional guidance, such as the California TB Risk Assessment tool, recommends LTBI testing and treatment for all non-US-born individuals, individuals with immunosuppressive conditions or taking immunosuppressive therapy, and individuals who have had contact with someone with infectious TB disease during their lifetime [22]. Recent modeling has demonstrated that adherence to such approaches could substantially reduce the burden of TB disease, reducing incidence by 40% [23].…”

Development and validation of a prediction algorithm to identify birth in countries with high tuberculosis incidence in two large California health systems

“…Future predictive models incorporating predicted birth in HTBIC along with other elements that may predict TB activation (immunosuppression, etc) may improve the predictive power and reduce the number needed to be screened overall. Even testing only 25% of eligible patients in primary care encounters could prevent a substantial amount of TB cases [ 25 ].…”

Policy Implications of Mathematical Modeling of Latent Tuberculosis Infection Testing and Treatment Strategies to Accelerate Tuberculosis Elimination

“…We summarize key results of NEEMA TB modeling activities that have been published or publicly presented and that have programmatic implications for which populations to test, which test to use, and which treatments are most cost effective. [17][18][19][20][21][22][23][24][25] A consensus of NEEMA modeling to date concurs with the aforementioned guidelines and suggests that, for greatest effectiveness and cost effectiveness, targeted testing policies should focus on LTBI testing and treatment among non-US-born persons and persons with HIV, respectively. Testing with an IGRA appears to be most cost effective.…”

“…23 A 23% increase in LTBI testing and treatment of all USPSTF-recommended populations at high risk 6 in California could prevent approximately 40% of new TB cases in the first decade post-implementation, with targeted testing and treatment of non-US-born persons having the greatest impact. 24…”