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

Figures & data

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

Input parameters	Point estimate	Sensitivity	Source
Incidence	1.28%	NA	De Graaf et al., 2013[23]
Disability weights depression
Mild (dw)	0.14	0.086 - 0.194	Stouthard et al., 1997[27]
Moderate (dw)	0.35	0.272 – 0.425	Stouthard et al., 1997[27]
Severe (dw)	0.76	0.556 – 0.971	Stouthard et al., 1997[27]
Depression duration
Mild	3 months	NA	based on Spijker et al., 2002[24]
Moderate	6 months	NA	based on Spijker et al., 2002[24]
Severe	9 months	NA	based on Spijker et al., 2002[24]
Background mortality	0.001984	NA	Based on life tables [36]
Calibration parameters	Point estimate	Sensitivity	To match outcome
severity distribution at incidence
Mild	30.0%	NA	distribution prevalence mild, moderate, severe depression as reported in Chisholm et al., 2004[35]
Moderate	47.0%	NA
Severe	23.0%	NA
Recurrence rates
after 1^st episode	59.8%	NA	recurrence rates as reported in Solomon et al., 2000[32]
after 2^nd episode	77.2%	NA
after 3^rd episode	84.2%	NA
after 4^th episode	91.1%	NA
after 5^th episode	92.9%	NA
Occurring after
one year	40%	NA	recurrence rates as reported in Solomon et al., 2000[32]
two years	30%	NA
three years	20%	NA
four years	7%	NA
five years	3%	NA
Excess mortality	1.65	NA	De Graaf et al., 2012[34]
Outcomes	Model outcome	Reference value	Source
Prevalence of depression	5.24%	5.21%	De Graaf et al., 2012[34]
Severity distribution
Mild	30.4%	30%	Chisholm et al., 2004[35]
Moderate	47.0%	47%	Chisholm et al., 2004[35]
Severe	22.7%	23%	Chisholm et al., 2004[35]
Recurrence rates	ranging from 24% (risk of recurrence after 1 year, given 1 previous episode) to 84% (risk of recurrence after 5 years given 4 previous episodes)	ranging from 25% (20%-32%) (risk of recurrence after 1 year, given 1 previous episode) to 79% (35%-95%) (risk of recurrence after 5 years given 3 previous episodes)	Solomon et al., 2000[32]

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

Intervention by depression severity level	Coverage rate	Compliance rate	Effect
Prevention of first incidence (targeting subclinical depression)	%	%	RD
E-health intervention (unsupported) ^1	0	80	0.077
Treatment of mild depression	%	%	d
E-health intervention (supported) ^2	2	43	0.33
Individual psychological intervention, primary care, 8 sessions ^3	17	56	0.51
Treatment of moderate depression	%	%	d
E-health intervention (supported) ^2	2	43	0.33
Individual psychological intervention, primary care, 8 sessions ^3	16	56	0.51
	%	%	d
Individual psychological outpatient care, 8–24 sessions ^3	18	68	0.51
Anti-depressants, 3–6 months via GP ^4	20	44	0.30
Anti-depressants, 3–6 months, with psychological support ^3	20	56	0.51
Prevention of recurrent depression	%	%	RR
Clinical management with maintenance medication, 12 months ^5	0	42	0.75
Mindfulness-based CT ^6	0	68	0.66
Cognitive (behavior) therapy ^6	0	56	0.68

¹Taken from van Zoonen et al. 2014 [15]; ² Taken from Karyotaki et al. 2017 [38]; ³ Taken from Cuijpers et al. 2019 [10]; ⁴ Taken from Cipriani et al. 2018 [39]; ⁵ Taken from Vittengl et al. 2007 [40]; ⁶ Taken from Biesheuvel-Leliefeld et al. 2015 [41].

Table 3. Intervention costs expressed in 2019 Euros

Intervention by depression severity level
Prevention of first incidence (targeting subclinical depression)	€*	Resource use
E-health intervention (unsupported)	160	1x GP; intake psychologist; 4–9 sessions unsupported online self-help; incl. hosting costs
Treatment of mild depression	€	Resource use
E-health intervention (supported)	265	1x GP; intake psychologist; 4–9 sessions online self-help; 4–5 telephone support prevention worker; hosting cost of the e-health intervention
Individual psychotherapy	962	1x GP; 8 sessions with a psychologist; coordination with GP
Treatment of moderate depression	€	Resource use
E-health intervention (supported)	265	1x GP; intake psychologist; 4–9 sessions online self-help; 4–6 telephone support prevention worker; hosting
Individual psychotherapy	962	1x GP; 8 sessions with a psychotherapist; coordination with GP
Treatment of severe depression	€	Resource use
Individual psychotherapy, outpatient care	1,984	1x GP; 8–24 sessions by a psychotherapist
Antidepressants	607	12 months of medication; contacts with GP or psychiatrist
Antidepressants with additional psychological support	679	12 months of medication; contacts with GP or psychiatrist; 3–6 visits GP support
Combination therapy (medication plus psychotherapy)	2,520	12 months of medication; contacts with GP or psychiatrist; 8–24 sessions with a psychotherapist
Prevention of recurrent depression	€	Resource use
Clinical management with maintenance medication	1,002	1x psychiatrist; 8–14 visits GP; 12 months of medication
Mindfulness-based CT	361	1x GP; 8 group sessions (11 participants on average)
Cognitive (behavior) therapy	429	1x GP; 8 group sessions (8 participants on average)

* Intervention costs were estimated by multiplying the guideline concordant resource use associated with each intervention by their standard (unit) cost price, as listed in the Dutch guideline for health-economic evaluation.

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 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 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

Table

	Total costs (95%CI)	Total QALYs (95%CI)	Incremental costs	Incremental QALYs	ICER
Reference scenario	€632,198 (€566,008 – €700,627)	36,662 (27,079–48,500)	NA	NA	NA
Alternative scenario	€823,304 (€748,926 – €898,681)	(55,069 (42,569–71,031)	€191,106	18,408	€10 per QALY gained

van Zoonen K, Buntrock C, Ebert DD, et al. Preventing the onset of major depressive disorder: a meta-analytic review of psychological interventions. Int J Epidemiol. 2014 Apr;43(2):318–329.

 PubMed Web of Science ®Google Scholar

Karyotaki E, Riper H, Twisk J, et al. Efficacy of self-guided internet-based cognitive behavioral therapy in the treatment of depressive symptoms: a meta-analysis of individual participant data. JAMA Psychiatry. 2017;74(4):351–359.

 PubMed Web of Science ®Google Scholar

Cuijpers P, van Straten A, van Schaik A, et al. Psychological treatment of depression in primary care: a meta-analysis. Br J Gen Pract. 2009;59(559):e51–e60.

 PubMed Web of Science ®Google Scholar

Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of antidepressant drugs in the acute treatment of major depressive disorder: a network meta-analysis. Lancet. 2018;391:1357–1366.

 PubMed Web of Science ®Google Scholar

Vittengl JR, Clark LA, Dunn TW, et al. Reducing relapse and recurrence in unipolar depression: a comparative meta-analysis of cognitive-behavioral therapy’s effects. J Consult Clin Psychol. 2007;75(3):475.

 PubMed Web of Science ®Google Scholar

Biesheuvel-Leliefeld KE, Kok GD, Bockting CL, et al. Effectiveness of psychological interventions in preventing recurrence of depressive disorder: meta-analysis and meta-regression. J Affect Disord. 2015;174:400–410.

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