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Original Research

Design of a health-economic Markov model to assess cost-effectiveness and budget impact of the prevention and treatment of depressive disorder

ORCID Icon, ORCID Icon, , , , , , ORCID Icon & show all
Pages 1031-1042 | Received 02 Sep 2020, Accepted 28 Oct 2020, Published online: 23 Nov 2020

Figures & data

Table 1. Parameters in DepMod (point estimates, range, justification)

Table 2. Illustration of selected evidence-based interventions by depression severity level: target group reached by the intervention expressed as Coverage (%) and Compliance with therapy (%). Effect expressed as risk difference (RD) or Relative Risk (RR) when impacting on transitions or as standardized effect size (Cohen’s d) when impacting on symptom severity, all representing average values

Table 3. Intervention costs expressed in 2019 Euros

Figure 1. Schematic overview of the model development process

Figure 1. Schematic overview of the model development process

Figure 2. Conceptual model of the course of depression serving as a starting point in the process of model development

Figure 2. Conceptual model of the course of depression serving as a starting point in the process of model development

Figure 3. Markov-model

Arrows representing all possible transitions from one state to another. In the model, each arrow is represented by a transition probability.
Figure 3. Markov-model

Figure 4. Internally consistent epidemiological structure of depression based on NEMESIS-studies (yearly number of people in each health state in parentheses)

Figure 4. Internally consistent epidemiological structure of depression based on NEMESIS-studies (yearly number of people in each health state in parentheses)

Figure 5. (a) Cost-effectiveness plane (top) and (b) cost-effectiveness acceptability curve (bottom) associated with scaling up prevention. The cost-effectiveness acceptability curve expresses the probability that scaling up prevention is cost-effective (y-axis) and on the x-axis the willingness to pay for one QALY gained given various ceiling ratios

Figure 5. (a) Cost-effectiveness plane (top) and (b) cost-effectiveness acceptability curve (bottom) associated with scaling up prevention. The cost-effectiveness acceptability curve expresses the probability that scaling up prevention is cost-effective (y-axis) and on the x-axis the willingness to pay for one QALY gained given various ceiling ratios