Table 1

Possible challenges of and approaches to health system modelling research

ChallengeDescriptionPotential approach
Formulation of model
  • The model should capture the dynamic interactions between the main components of the health system and acknowledge constraints.

  • Deciding on which components and constraints to include depends on what is most relevant to the problem under study and data availability.

  • Assembling all available datasets before model conceptualisation enables modellers to recognise what should be included in the model.

  • Examples of datasets include: household surveys for population health and socioeconomic characteristics, geographic information systems for facility locations and human resource allocations, health management information systems for drug stocks and patient visits.

Parametrisation of model
  • The model can consist of many parameters that need to be identified.

  • In addition to the difficulty of dealing with a large parameter space, some parameters are qualitative in nature (eg, skills of workforce, quality of education, political feasibility).

  • Parameters can be extracted from a plethora of datasets from multiple sources such as: Demographic and Health Surveys, Multiple Indicator Cluster Surveys, Service Provision Assessment, Service Availability and Readiness Assessment, National Health Accounts, World Development Indicators, Global Burden of Disease Study.

  • As for qualitative features, numerical values may be assigned to different categories of a certain variable (eg, for rating workforce skills) and a variety of scenario analyses can be conducted.

Validation of model
  • Multiple distinct datasets should be used to validate the model.

  • Multiple intermediate indicators and outputs (eg, coverage of health services) along with outcomes (eg, disease incidence) should be monitored.

Large simulations
  • The model can consist of many compartments and routines depending on both space and time (eg, location of health facilities and road networks relative to population distribution, drug supply chain) which requires the use of large simulations.

  • High performance computing and parallel programming can be used to perform such required large simulations.

Presentation of model results
  • The model can consist of multiple outcomes to be evaluated.

  • The model should allow a clear comparison of impact between HSS interventions.

  • Present model findings with the use of dashboards displaying all possible outcomes or with aggregating outcomes using weights.

  • Define the limits of the impact of HSS interventions and the status quo.

Data gaps
  • A large data repository is preferable to build a complete model, yet data gaps are inevitable.

  • Once gaps are identified, data collection can be pursued to improve future versions of the model.