Program Evaluation Resources for Modeling and Costs

Updated December 8, 2021

The NCHHSTP Epidemiologic and Economic Modeling Agreement (NEEMA) supports a wide range of modeling activities and interactive tools, including those that estimate:

Projections of Morbidity and Mortality
Burden and Costs of Diseases
Costs and Cost-effectiveness of Interventions
Population-level Program Impact
Optimized Resource Allocation

Resources for Modeling & Costs

Multiple tools are available for public use to help estimate the costs to public health programs for different interventions. A recent summary categorizes and describes some of those tools.

Contreary K, Chen ZA, Chattopadhyay S, Chang MH. A review of tools to calculate the cost of health conditions and common health risk factors. J Public Health Manage Pract 2015;21:E1–10. (Abstract available at https://pubmed.ncbi.nlm.nih.gov/25581273)

Planning for TB Elimination using Tabby2

An interactive webtool is now available for US states to estimate future US tuberculosis (TB) and latent TB infection (LTBI) cases based on a published TB transmission model.¹ The tool, available at https://ppmltools.org/tabby2/, provides projections based on state-specific historical trends, in addition to national projections based on national trends. Tabby2 allows users to choose pre-defined or to specify custom intervention scenarios to compare with baseline projections through 2050. The tool also allows users to calculate the number and cost of LTBI tests, LTBI treatments, and consequent TB disease cases, as well as the cost effectiveness of interventions. States can use Tabby2 to set informed targets to make a compelling case to further TB elimination. If you have any questions about the tool, please contact smarks@cdc.gov.

Tabby2 Slides [PDF – 2MB] and Narrative [PDF – 175KB]
¹ Menzies NA, Cohen T, Hill AN, Yaesoubi R, Galer K, Wolf E, Marks SM, Salomon JA. Prospects for Tuberculosis Elimination in the United States: Results of a Transmission Dynamic Model. American Journal of Epidemiology. 2018. https://www.ncbi.nlm.nih.gov/pubmed/29762657

A Tool to Estimate Costs and Savings of Tuberculosis Directly Observed Therapy using Video

Has your TB control program asked the questions?

What is the cost of starting a new video directly observed
therapy (video DOT) program?

How much time could we save by switching to video DOT?
What are the comparative cost estimates of video DOT and
traditional in-person DOT?

CDC developed the video DOT Costing Tool to help health departments answer these and other questions about the costs and savings of implementing a video DOT program (also referred to as electronic DOT or eDOT).

The video DOT cost calculator tool is designed to be easy to use. The only requirements to use the tool are the ability to run Microsoft Excel 2007 (or newer) and 3 basic data elements:

the number of patients who use video DOT
the expected cost of the video DOT software platform
whether or not the program will be providing smartphones for patients.

Here’s what users are saying about the video DOT cost calculator tool:

“We would like to be able to project costs to implement vDOT into our budget. We are currently starting with a small pilot and would like to use this tool to help inform the future implementation of vDOT…”
“Our local health department with the highest TB incidence piloted the tool for several months … they were able to use the system [tool] to identify actual expenses saved by using vDOT.”

To use the tool to estimate or compare video DOT and traditional in-person DOT costs in your TB program, please email your consultation request to TBPEN@cdc.gov. The tool is only available by request.

Development of this tool was based on an economic evaluation of program and patient costs associated with video DOT in a U.S.-based high, medium and low incident setting. This article describes the evaluation in more detail.¹

¹Garrett R. Beeler Asay, et al. Cost of Tuberculosis Therapy Directly Observed on Video for Health Departments and Patients in New York City; San Francisco, California; and Rhode Island (2017–2018), American Journal of Public Health 110, no. 11 (November 1, 2020): pp. 1696-1703. https://ajph.aphapublications.org/doi/10.2105/AJPH.2020.305877