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Value of Information Analysis Applied to the Economic Evaluation of Interventions Aimed at Reducing Juvenile Delinquency: An Illustration.

Eeren HV, Schawo SJ, Scholte RH, Busschbach JJ, Hakkaart L - PLoS ONE (2015)

Bottom Line: Further research can reduce that parameter uncertainty.Therefore, in this illustrative analysis, the value of information analysis determined that society should be willing to spend a maximum of €176 million in reducing decision uncertainty in the cost-effectiveness of the two interventions.Moreover, the results suggest that reducing uncertainty in some specific model parameters might be more valuable than in others.

View Article: PubMed Central - PubMed

Affiliation: Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, the Netherlands; Department of Psychiatry, section Medical Psychology and Psychotherapy, Erasmus Medical Center, Rotterdam, the Netherlands; Viersprong Institute for Studies on Personality Disorders (VISPD), Halsteren, the Netherlands.

ABSTRACT

Objectives: To investigate whether a value of information analysis, commonly applied in health care evaluations, is feasible and meaningful in the field of crime prevention.

Methods: Interventions aimed at reducing juvenile delinquency are increasingly being evaluated according to their cost-effectiveness. Results of cost-effectiveness models are subject to uncertainty in their cost and effect estimates. Further research can reduce that parameter uncertainty. The value of such further research can be estimated using a value of information analysis, as illustrated in the current study. We built upon an earlier published cost-effectiveness model that demonstrated the comparison of two interventions aimed at reducing juvenile delinquency. Outcomes were presented as costs per criminal activity free year.

Results: At a societal willingness-to-pay of €71,700 per criminal activity free year, further research to eliminate parameter uncertainty was valued at €176 million. Therefore, in this illustrative analysis, the value of information analysis determined that society should be willing to spend a maximum of €176 million in reducing decision uncertainty in the cost-effectiveness of the two interventions. Moreover, the results suggest that reducing uncertainty in some specific model parameters might be more valuable than in others.

Conclusions: Using a value of information framework to assess the value of conducting further research in the field of crime prevention proved to be feasible. The results were meaningful and can be interpreted according to health care evaluation studies. This analysis can be helpful in justifying additional research funds to further inform the reimbursement decision in regard to interventions for juvenile delinquents.

No MeSH data available.


Related in: MedlinePlus

Markov model.nmr = natural mortality rate. tpA2A = transition probability of staying in state A. tpA2B = transition probability of moving from state A to state B. tpB2A = transition probability of moving from state B to state A. tpB2B = transition probability of staying in state B.
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pone.0131255.g001: Markov model.nmr = natural mortality rate. tpA2A = transition probability of staying in state A. tpA2B = transition probability of moving from state A to state B. tpB2A = transition probability of moving from state B to state A. tpB2B = transition probability of staying in state B.

Mentions: The Markov model that was used for the value of information analysis consists of three mutually exclusive model states: A) criminal behaviour, B) no criminal behaviour, and C) dead [25] (Fig 1). The time horizon of the model was 20 years, with a cycle length of six months [25]. A societal perspective was taken and results were expressed as costs per Criminal Activity Free Year (CAFY) [25].


Value of Information Analysis Applied to the Economic Evaluation of Interventions Aimed at Reducing Juvenile Delinquency: An Illustration.

Eeren HV, Schawo SJ, Scholte RH, Busschbach JJ, Hakkaart L - PLoS ONE (2015)

Markov model.nmr = natural mortality rate. tpA2A = transition probability of staying in state A. tpA2B = transition probability of moving from state A to state B. tpB2A = transition probability of moving from state B to state A. tpB2B = transition probability of staying in state B.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493049&req=5

pone.0131255.g001: Markov model.nmr = natural mortality rate. tpA2A = transition probability of staying in state A. tpA2B = transition probability of moving from state A to state B. tpB2A = transition probability of moving from state B to state A. tpB2B = transition probability of staying in state B.
Mentions: The Markov model that was used for the value of information analysis consists of three mutually exclusive model states: A) criminal behaviour, B) no criminal behaviour, and C) dead [25] (Fig 1). The time horizon of the model was 20 years, with a cycle length of six months [25]. A societal perspective was taken and results were expressed as costs per Criminal Activity Free Year (CAFY) [25].

Bottom Line: Further research can reduce that parameter uncertainty.Therefore, in this illustrative analysis, the value of information analysis determined that society should be willing to spend a maximum of €176 million in reducing decision uncertainty in the cost-effectiveness of the two interventions.Moreover, the results suggest that reducing uncertainty in some specific model parameters might be more valuable than in others.

View Article: PubMed Central - PubMed

Affiliation: Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, the Netherlands; Department of Psychiatry, section Medical Psychology and Psychotherapy, Erasmus Medical Center, Rotterdam, the Netherlands; Viersprong Institute for Studies on Personality Disorders (VISPD), Halsteren, the Netherlands.

ABSTRACT

Objectives: To investigate whether a value of information analysis, commonly applied in health care evaluations, is feasible and meaningful in the field of crime prevention.

Methods: Interventions aimed at reducing juvenile delinquency are increasingly being evaluated according to their cost-effectiveness. Results of cost-effectiveness models are subject to uncertainty in their cost and effect estimates. Further research can reduce that parameter uncertainty. The value of such further research can be estimated using a value of information analysis, as illustrated in the current study. We built upon an earlier published cost-effectiveness model that demonstrated the comparison of two interventions aimed at reducing juvenile delinquency. Outcomes were presented as costs per criminal activity free year.

Results: At a societal willingness-to-pay of €71,700 per criminal activity free year, further research to eliminate parameter uncertainty was valued at €176 million. Therefore, in this illustrative analysis, the value of information analysis determined that society should be willing to spend a maximum of €176 million in reducing decision uncertainty in the cost-effectiveness of the two interventions. Moreover, the results suggest that reducing uncertainty in some specific model parameters might be more valuable than in others.

Conclusions: Using a value of information framework to assess the value of conducting further research in the field of crime prevention proved to be feasible. The results were meaningful and can be interpreted according to health care evaluation studies. This analysis can be helpful in justifying additional research funds to further inform the reimbursement decision in regard to interventions for juvenile delinquents.

No MeSH data available.


Related in: MedlinePlus