NCDSim: A simulation Model for Forecasting NCD Burden and Evaluating Public Health Interventions

Abstract

NCDSim is a system-dynamics simulation model developed by the Swedish Public Health Agency, in collaboration with the Swedish Cancer Society and the Swedish Heart-Lung Foundation, to project the future burden of non-communicable diseases (NCDs) such as cancer and cardiovascular diseases in Sweden. The model integrates demographic projections with epidemiological data on risk factors (e.g., alcohol, physical inactivity, smoking, diet), disease incidence, and mortality. It simulates four population stocks (individuals with cancer, cardiovascular disease, both conditions, and those without either disease) and ten non demographic annual flows (incidence, recovery) stratified by age, sex, and risk factor exposure. Using population attributable fractions and relative risks, NCDSim quantifies the impact of risk factors on disease incidence. It also estimates direct and indirect costs, supporting cost-effectiveness analyses. The model is implemented in R with a Shiny-based interface for scenario analysis and visualization. NCDSim enables users to simulate the effects of preventive interventions, compare scenarios against a baseline, and assess long-term outcomes up to 2120. Its flexibility and data-driven approach make it a valuable tool for public health policy, health impact assessments, and evidence-based decision-making.

Key messages: • NCDSim simulates future NCD burden, helping policymakers evaluate and prioritize preventive interventions.

• NCDSim enables evidence-based public health planning by projecting disease burden and assessing interventions’ impact on health and costs.

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Cite

BibTeX RIS

BibTeX

@article{benzi2026ncdsim,
  title = {{NCDSim: A simulation Model for Forecasting NCD Burden and Evaluating Public Health Interventions}},
  author = {F Benzi and T Ekerby and L Brouwers and H Källberg and M Berlin and A Jansson and E Ramfalk},
  journal = {The European Journal of Public Health},
  year = {2026},
  month = oct,
  doi = {10.1093/eurpub/ckag169.1883},
  url = {https://doi.org/10.1093/eurpub/ckag169.1883},
  note = {CC BY-NC 4.0},
}

RIS

TY  - JOUR
TI  - NCDSim: A simulation Model for Forecasting NCD Burden and Evaluating Public Health Interventions
AU  - F Benzi
AU  - T Ekerby
AU  - L Brouwers
AU  - H Källberg
AU  - M Berlin
AU  - A Jansson
AU  - E Ramfalk
PY  - 2026
DA  - 2026/10/01
JO  - The European Journal of Public Health
DO  - 10.1093/eurpub/ckag169.1883
UR  - https://doi.org/10.1093/eurpub/ckag169.1883
AB  - NCDSim is a system-dynamics simulation model developed by the Swedish Public Health Agency, in collaboration with the Swedish Cancer Society and the Swedish Heart-Lung Foundation, to project the future burden of non-communicable diseases (NCDs) such as cancer and cardiovascular diseases in Sweden. The model integrates demographic projections with epidemiological data on risk factors (e.g., alcohol, physical inactivity, smoking, diet), disease incidence, and mortality. It simulates four population stocks (individuals with cancer, cardiovascular disease, both conditions, and those without either disease) and ten non demographic annual flows (incidence, recovery) stratified by age, sex, and risk factor exposure. Using population attributable fractions and relative risks, NCDSim quantifies the impact of risk factors on disease incidence. It also estimates direct and indirect costs, supporting cost-effectiveness analyses. The model is implemented in R with a Shiny-based interface for scenario analysis and visualization. NCDSim enables users to simulate the effects of preventive interventions, compare scenarios against a baseline, and assess long-term outcomes up to 2120. Its flexibility and data-driven approach make it a valuable tool for public health policy, health impact assessments, and evidence-based decision-making.  Key messages: • NCDSim simulates future NCD burden, helping policymakers evaluate and prioritize preventive interventions.  • NCDSim enables evidence-based public health planning by projecting disease burden and assessing interventions’ impact on health and costs.
SN  - 1101-1262
SN  - 1464-360X
ER  -

Abstract

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