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DCPSim: Using agent-based modeling to compare health interventions

DCPSim: Using agent-based modeling to compare health interventions

For health policy makers, knowing what health interventions will lead to cost-effective health benefits is only half the battle.  They need further information to determine how these interventions should be implemented in order to get the greatest benefit for the least cost. 

While cost-effectiveness analysis (CEA) can provide information of the cost per health gain for a specific intervention, extended cost-effectiveness analysis (ECEA) takes this one step further by examining the health and non-health impacts of policy changes.  ECEA examines a policy, such as universal public financing (UPF) of health care, and traces the benefits beyond lives saved to financial risk protection and the shift from out-of-pocket private health payments to public spending, while at the same time examining the distribution of these impacts across different subsets of the population.  Armed with this information, policy makers can compare the effects of various health policies as they consider how to implement new health care interventions. 

As part of the Disease Control Priorities Network (DCPN), CDDEP is developing a new model, called DCPSim, to conduct ECEA for various health policy changes in India.

At the heart of the model is a simulated population, approximately 1 million people whose background characteristics determine their probability of acquiring a disease and their behavioral response to falling ill.  The in silico population is created using a representative household dataset from India so that its socio-economic and epidemiological characteristics represent the actual population.

Based on the information in this data, members of the population have the potential to acquire a disease, seek treatment from a particular facility, and recover, each with a probability specific to their socio-economic characteristics.  By running the model over several years, we can examine the following outcomes estimated by the DCPSim model: epidemiological indicators such as disease incidence and mortality, cost-effectiveness of a treatment in terms of dollars per death averted and disability-adjusted life years (DALY) saved, and the extended cost effectiveness outcomes of a treatment, such as private out-of-pocket expenditure averted, distributional consequences of UPF, and the value of insurance provided.  In addition to presenting the mean impact we can disaggregate the results by gender, age groups, standard of living, and geographic location. 

In an ideal world, new health policies could be evaluated through case studies in areas that have already implemented the changes, or through using randomized control trials to compare outcomes.  In reality, conducting such studies around the world would be far too costly and inefficient.  By creating a simulated population of agents and evaluating the impact of interventions on these agents, DCPSim provides a detailed analysis of numerous interventions for a wide array of diseases and conditions in one model.  By changing the parameters of the model, we can move from running an ECEA of financing epilepsy treatments in one state to understanding the impacts of starting a new training program for surgeons in a different state without having to finance separate trials. 


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