The Coalition for Applied Modeling for Prevention (CAMP) is dedicated to creating models that improve public health decision-making at the national, state, and local levels. We use statistical, epidemic simulation, and economic models to uncover new disease patterns and inform prevention policies in five areas: HIV, viral hepatitis, sexually transmitted infections, tuberculosis, and school/adolescent health.
We are made up of experts from a variety of fields - epidemiologists, economic and infectious disease modelers, physicians, economists, and health department representatives - working in partnership with leaders at the US Centers for Disease Control and Prevention.
CAMP has published a variety of high-impact scientific papers that tackle tough public health questions and has released interactive web tools that guide decision-making.
Explore our website to learn more about our work and the CAMP team. Thank you for visiting!This work is supported by The Centers for Disease Control and Prevention [Grant # 1 1 NU38PS004650]
Hamilton D,Rosenberg E,Sullivan P,Wang L,Dunville R,Barrios L,Aslam M,Mustanski B,Goodreau S
Marks S,Dowdy D,Menzies N,Shete P,Salomon J,Parriott A,Shrestha S,Flood J
Tuberculosis (TB) disease is the leading cause of death globally from a single infectious organism.1 However, TB is both curable and preventable. In the United States during the past 2 decades, a national coordinated multi-agency policy response implemented in 1992, along with other influences (eg, new federal and state funding), led to a decrease in the number of TB cases reported in the United States, from 26 673 in 1992 to 9105 in 2017, a 65.9% decline.2,3 The 1992 national policy response was launched as a result of multidrug-resistant TB outbreaks that occurred during 1985-1992. That response included support for improved TB diagnostics, infection control, monitoring of TB treatment, and investigation of persons who had recent contact with persons who had infectious TB.4 Mathematical modeling of TB during 1995-2014 in the United States estimated that approximately 145 000 to 319 000 TB cases were averted, yielding societal benefits (in 2014 US dollars) of $3.1 billion to $14.5 billion.4
Recently, however, the US decline in the number of TB cases and in the incidence of TB has slowed substantially. During 2014-2017, the annual percentage decrease in the number of TB cases and in the TB incidence rate was <2% annually compared with 4%-5% during the previous decade.3 An estimated 13% of US TB cases (and only 8% of US TB cases among non–US-born persons) is attributed to recent transmission, with nearly all the remaining TB cases assumed to be due to reactivation of latent TB infection (LTBI).3 To address this slowing in TB decline, the Centers for Disease Control and Prevention (CDC) recognized the need to improve prevention of LTBI reactivation in its recommendations to expand LTBI testing with interferon gamma release assays (IGRAs) and use of short LTBI treatment regimens among persons at greatest risk of TB.5-9
Because guidelines for LTBI testing and treatment in the United States can be difficult to interpret, we summarize the current guidelines and then present results of recent mathematical modeling of the impact of LTBI testing and treatment in furthering US TB elimination. We identify populations that were modeled for LTBI testing and treatment that resulted in the greatest future TB reduction and those that were the most efficient to test and treat. We also summarize results on the most cost-effective test and treatment to use in specific populations. Consistent findings across models are highlighted. Finally, we discuss challenges to translating modeling into policies and to implementing the modeled scenarios, as well as possible future modeling.
Jenness S,Johnson J,Hoover K,Smith D,Delaney K