Table 2 Summary table of epidemiological modeling structures for the economic evaluation of non-communicable disease public health interventions

Section: Decision trees

Decision tree

Can be easy to construct.

No explicit time component.

Comparing exercise referral schemes with usual care to increase physical activity [29].

Relatively easy to interpret.

Exponentially more complex with additional disease states.

Table 1: A1, B1, C1, D1

Can be adapted for cohorts and individuals.

No looping/recurring.

Poorly suited to complex scenarios.

Section: Comparative riskassessment

Comparative risk assessment

Can model multiple diseases and risk factors simultaneously.

More complex to build than decision trees.

Return on investment of workplace interventions to improve physical activity [32].

Can be used for individuals or cohorts.

No explicit time component.

No looping/recurring.

Table 1: A1, B1, C1, D1

Unable to model interactions between individuals, populations, or their environment.

Section: Markov models without interaction

Markov models without interaction

Relatively straightforward to construct and to communicate.

The Markovian assumption-individuals have no memory of (are independent of) previous disease states.

Investigating the cost effectiveness of different smoking cessation strategies using the Benefits of Smoking Cessation on Outcomes (BENESCO) model [33–35].

Can model populations or individuals.

Table 1: A2, B2, C2, D2

Has time component.

Can only exist in one disease state.

Allows looping/recurring.

Exponential increase in complexity with increasing numbers of disease states.

Section: System dynamics models

System dynamics models

Allows for interactions between populations and the environment.

Models populations rather than individuals.

Modeling the effects of policies aimed at increasing bicycle commuting rather than travelling by car [63].

Table 1: A3, A4

Allows for feedback and recurring.

Section: Markov chain models and individual-level Markov models with interaction

Markov chain models and Markov individual event history models

Can model individuals or populations.

Markovian assumption still exists (although its impact can be reduced-see main text).

A CDC model evaluating the cost-effectiveness of different diabetes prevention strategies [58, 59].

Table 1: B3, B4, C3, C4

Allows for interaction between populations or individuals within the model.

Becomes rapidly more complex with added disease states.

Section: Discrete event simulation

Discrete event simulation

Allows for interaction between individuals and between individuals, populations, and their environment, governed by system rules.

Model structure can be difficult to communicate and interpret.

Evaluating the cost-effectiveness of screening programs [67].

Table 1: D3, D4

Computationally challenging both in terms of designing the model and running it.

Allows for modeling of complex scenarios.

Section: Agent-based simulation

Agent-based simulation

Allow for interactions within and between individuals, populations, and the environment, governed by rules applied to individuals.

More complex than discrete event simulation.

The Archimedes model for modeling the outcomes of changing health care systems, such as investigating diabetes care [70].

Table 1: D5

Requires large computational power.

Allows for individuals to learn.

Difficult to communicate and interpret model structure.

Allows modeling of complicated systems.

Table 1: adjunct to A1, B1, C1, D1, A2, B2, C2, D2

Multistate life tables

Can be used with comparative risk assessment and decision tree models to add a time component.

Assumes diseases are independent of each other.

The Australian Assessing Cost Effectiveness in Prevention (ACE Prevention) project [74, 76].

Can be combined with Markov models to increase the numbers of possible disease states without exponentially increasing model complexity.

Model limited by underlying model structure, for example, if combined with a Markov model, the Markovian assumption remains.

Table 1: adjunct to C1, C2, C3, C4, D1, D2

Microsimulation

Can be combined with decision tree, comparative risk assessment, and Markov models to make it easier to model heterogeneous populations or multiple disease states.

Data requirements and simulations can become computationally challenging with complex models.

The NICE obesity health economic model used by Trueman et al. to estimate the cost-effectiveness of primary care weight management programs [83].

Model limited by underlying model structure, for example, if combined with a Markov model, the Markovian assumption remains.