STAR: A New Technology to Evaluate the Future Impact of Chronic Diseases and Health Care Decisions

 

Scientific Study Title:

Microsimulation Modeling of the Impact of Health Interventions and Policies: Simulation Technology for Applied Research (STAR).

 

Study Start Date:

2008

Study End Date:

2013

 

Why Did We Do This Research?

The goal of the Simulation Technology for Applied Research (STAR) project was to show that computer-based models of chronic diseases are beneficial in predicting the effects of public health policies. Computer simulation modeling is a process that allows us to see how these policies will affect the population before they are implemented. Scientists will be able to use these models to learn how risk factors affect the future well-being of the population. Increased use of computer models of health and disease will improve decision-making at the local, provincial, and national levels and will help reduce the burden of disease in Canada.

What Did We Do?

We used a computer program called POHEM (Population Health Model) to predict which public health interventions and policies should be used to reduce the burden of major chronic conditions, such as heart disease, diabetes, and arthritis.  It was developed at Statistics Canada.  The key to the STAR team’s success was its strong group efforts and the diverse talents within the team. This group is experienced in Health Services Research, Epidemiology, Statistics, Medicine, Computer Science, Simulation Modelling, Health Economics and many other fields. The STAR team also worked closely with Government Officials to improve policy-related research. The modeling technique we used is called microsimulation.  It simulated individual “people” to track the effect on the population.  It then computed the risk of different diseases and predicted their effects on health.

What Did We Find?

Our research showed that the results from simulation models are trustworthy and can help both scientists and decision-makers. We have published an outline for the validation of these models. The POHEM model has been extensively confirmed. Except for obesity and diabetes, we have found that all other risk factors for heart disease were expected to decrease. Obesity was projected to surpass smoking as the most prevalent risk factor. The total cost of osteoarthritis (OA) was predicted to increase from $2.9 billion to $7.6 billion between 2010 and 2031, a 2.6-fold increase. OA prevalence was projected to grow from 13.9 to 18.4% between 2010 and 2030.

 

The Research Team:

Principal Investigator:

Jacek Kopec, PhD, Research Scientist Emeritus, Arthritis Research Canada (University of British Columbia)

Co-Investigators:

Michal Abrahamowicz, PhD, Research Scientist, Arthritis Research Canada (McGill University)

David Buckeridge – MD, PhD (McGill University)

Philippe Fines – PhD (Statistics Canada)

William Flanagan (Statistics Canada)

Sam Harper MSPH – PhD (McGill University)

John Lynch – PhD, MPH (University of South Australia)

Douglas Manuel – MD, FRCPC (University of Ottawa)

Jillian Orderkirk (Organisation for Economic Co-operation and Development)

Michael Wolfson – PhD (University of Ottawa)

 

Who Funded This Research?

This study was funded by the Canadian Institutes of Health Research (CIHR).

 

Related Publications:

  • Kopec JA, Sayre EC, Okhmatovskaia A, Cibere J, Li LC, Bansback N, Wong H, Ghanbarian S, Esdaile JM. A comparison of three strategies to reduce the burden of osteoarthritis: A population-based microsimulation study. PLoS One. 2021 Dec 8;16(12):e0261017.
  • Rahman MM, Cibere J, Anis AH, Goldsmith CH, Kopec JA. Risk of Type 2 Diabetes among Osteoarthritis Patients in a Prospective Longitudinal Study. Int J Rheumatol. 2014;2014:620920. doi:10.1155/2014/620920
  • Manuel DG, Ho TH, Harper S, Anderson GM, Lynch J, Rosella LC. Modelling preventive effectiveness to estimate the equity tipping point: at what coverage can individual preventive interventions reduce socioeconomic disparities in diabetes risk?. Chronic Dis Inj Can. 2014;34(2-3):94-102.
  • Wynant W, Abrahamowicz M. Impact of the model-building strategy on inference about nonlinear and time-dependent covariate effects in survival analysis. Stat Med. 2014;33(19):3318-3337. doi:10.1002/sim.6178
  • Manuel DG, Tuna M, Hennessy D, et al. Projections of preventable risks for cardiovascular disease in Canada to 2021: a microsimulation modelling approach. CMAJ Open. 2014;2(2):E94-E101. Published 2014 May 20. doi:10.9778/cmajo.2012-0015
  • Taljaard M, Tuna M, Bennett C, et al. Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol. BMJ open. 2014;4(10):e006701-e006701. doi:10.1136/bmjopen-2014-006701
  • Rahman MM, Cibere J, Goldsmith CH, Anis AH, Kopec JA. Osteoarthritis incidence and trends in administrative health records from British Columbia, Canada. J Rheumatol. 2014;41(6):1147-1154. doi:10.3899/jrheum.131011
  • Smith BT, Smith PM, Harper S, Manuel DG, Mustard CA. Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions. J Epidemiol Community Health. 2014;68(4):384-389. doi:10.1136/jech-2013-202756
  • Nadeau C, Wong SL, Flanagan WM, et al. Development of a population-based microsimulation mode of physical activity in Canada. Health Rep. 2013;24(10):11-19.
  • Manuel DG, Rosella LC, Tuna M, Bennett C, Stukel TA. Effectiveness of community-wide and individual high-risk strategies to prevent diabetes: a modelling study. PLoS One. 2013;8(1):e52963. doi:10.1371/journal.pone.0052963
  • Sharif B, Wong H, Anis AH, Kopec JA. A Practical ANOVA Approach for Uncertainty Analysis in Population-Based Disease Microsimulation Models. Value Health. 2017;20(4):710-717. doi:10.1016/j.jval.2017.01.002
  • Bennett, C., & Manuel, D. G. (2012). Reporting guidelines for modelling studies. BMC medical research methodology, 12, 168. https://doi.org/10.1186/1471-2288-12-168
  • Kopec JA, Edwards K, Manuel DG, Rutter CM. Advances in Microsimulation Modeling of Population Health Determinants, Diseases, and Outcomes. Epidemiology research international. 2012;2012:1-3. doi:10.1155/2012/584739
  • Manuel DG, Rosella LC, Hennessy D, Sanmartin C, Wilson K. Predictive risk algorithms in a population setting: an overview. J Epidemiol Community Health. 2012;66(10):859-865. doi:10.1136/jech-2012-200971
  • Rowe G, Tremblay MS, Manuel DG. Can We Make Time for Physical Activity? Simulating Effects of Daily Physical Activity on Mortality. Epidemiology research international. 2012;2012:1-10. doi:10.1155/2012/304937
  • Kopec JA, Finès P, Manuel DG, et al. Validation of population-based disease simulation models: a review of concepts and methods. BMC Public Health. 2010;10:710. Published 2010 Nov 18. doi:10.1186/1471-2458-10-710