where B = benefits (such as QALYs or depression-free days), C = costs, int = intervention group, comp = comparator group.
Within the healthcare sector, the tools of economic evaluation are becoming more commonly used to help governments decide which interventions should be funded. For example, both the UK, via the National Institute of Health and Care Excellence (NICE), and Australia, via the Pharmaceutical Benefits Advisory Committee (PBAC) and the Medical Services Advisory Committee (MSAC), require formal evidence of cost-effectiveness to inform decisions of whether interventions should be eligible for public funding.
To complicate matters, different forms of economic evaluation are commonly used and confused in the literature. All forms tend to measure costs in the same way. Cost measurement includes costs associated with delivering the intervention and potentially affected by the intervention (for example, a consequence of an intervention may be an increase in general practitioner visits and a decrease in hospital costs). Ideally, all costs associated with interventions should be included regardless of whether they occur in the health sector or not. Pragmatically, however, it is difficult to measure all such costs – furthermore, the decision context of governments tends to be narrower (e.g., health departments tend to be primarily interested in health-budget impacts). The distinguishing feature of the various forms of economic evaluation is how benefits are measured. The first form of economic evaluation is cost-benefit analysis (CBA). In CBA, both costs and benefits are measured in monetary terms. The second main form is cost-utility analysis (CUA). In CUA, outcomes are measured with a generic metric that aims to combine both time and quality of life (that is, mortality and morbidity effects) by using preference-based techniques. The best known of these outcomes measures is the quality-adjusted life-year (QALY). The QALY is determined by simply multiplying the length of time in a particular health state by a weight to denote the preference value associated with that health state. The weights are defined on a scale of 0 to 1 where 0 denotes death and 1 denotes perfect health. The final major form of economic evaluation is cost-effectiveness analysis (CEA). In CEA, outcomes are measured with physical units that have clinical significance (such as proportion of people with a diagnosed anxiety disorder).
From an economics perspective, studies which allow comparability between different interventions tend to be preferred because decisions regarding competing uses of scarce resources can be made. It is for this reason that CBA tends to be the preferred technique. Since costs and consequences (or outcomes) are expressed in monetary terms, governments can potentially compare interventions across different sectors; for example, transport sector interventions can be compared to those in the health sector. However, the monetarization of health outcomes is a highly contentious and controversial issue, and CBA is not commonly used within the health sector (Drummond et al., 2005). In health, CUA is often preferred because it allows meaningful comparisons between different types of health interventions. For example, while it may be initially difficult to compare an intervention for the treatment of anxiety disorders to an intervention which promotes healthy eating in children to reduce obesity, if both these interventions express outcomes as QALYs, then they can be compared.
The way economic evaluations are commonly used to guide decisions is to specify a threshold of what is considered value-for-money. This is commonly done for QALY-like outcomes. For example, in Australia, a priority-setting approach based on the tools of economic evaluation uses a threshold value of AU$50,000 per DALY averted as a “value-for-money” criterion (Vos et al., 2010). Likewise, the WHO Commission on Macroeconomics and Health developed a rule-of-thumb criterion that an intervention averting less than 1 DALY for less than the average per capita income for a country is very cost-effective, and those that cost less than three times the average per capita income are still cost-effective (WHO Commission on Macroeconomics and Health, 2001). In the UK, NICE uses a threshold of £20,000–30,000 per QALY; however, it has been argued that this value may be too low (Towse, 2009). Clearly, it is difficult to derive such criteria of value-for-money when nongeneric measures are used. For example, it is difficult to know whether an intervention that costs AU$1,000 per point improvement on an anxiety symptom scale is good or bad value-for-money.
What is known about cost-effective interventions for parents with mental illness?
Unfortunately, there is a dearth of studies which have investigated the cost-effectiveness of interventions designed to treat or prevent parental mental illness. There is also a lack of economic evaluation studies which have investigated interventions targeting the children of parents with serious mental illness. There are, of course, quite a few studies which have investigated the cost-effectiveness of various treatments for mental disorders more generally in both adults and children (e.g., Bereza et al., 2009; Kilian et al., 2010; Mihalopoulos and Vos, 2013; Pirraglia et al., 2004; Wu et al., 2012). These studies all show that there are numerous interventions for the treatment of the major mental disorders (such as depression and anxiety) which are both effective and cost-effective. Furthermore, there is no reason to believe that these results do not also apply to parents with mental disorders. For example cognitive-behavioral therapy has been consistently shown to be an effective and cost-effective treatment for high-prevalence disorders such as anxiety and depression (Vos et al., 2005).
Recently, Bee et al. (2014) reviewed the effectiveness and cost-effectiveness of community-based interventions aimed at improving or maintaining quality of life in the children of parents with serious mental illness. This review concluded that there is little evidence currently available regarding the cost-effectiveness of such interventions. The only economic evaluation study found by the review evaluated a specialist psychiatric parent and baby day unit as compared with routine primary care in the treatment of postnatal depression. Importantly, this study did not appear to include any outcome measures related to the child, so it is not immediately clear why this study was included in the Bee et al. (2014) review. Other studies have evaluated interventions designed to prevent postnatal depression from developing (Mihalopoulos et al., 2011; Petrou et al., 2006). The interventions evaluated in these studies tend to be “indicated” preventive interventions. Indicated prevention refers to interventions which target people who are already showing signs of a disorder but do not meet the full diagnostic criteria of the disorder (Mrazek and Haggerty, 1994). These studies have found that interventions which screen women to identify those “at risk” of developing postnatal depression and provide a subsequent psychological intervention may be cost-effective, if they are effective. However, both Petrou et al. (2006) and Mihalopoulos et al. (2011) did not find strong evidence that such interventions are effective. For example, in a meta-analytic synthesis of study outcomes of eight trials, Mihalopoulos et al. (2011) found a relative risk of 0.68 (p = .07, 0.46–1.02) of intervention group women developing postnatal depression at follow-up.
There is also a growing literature which has found that depression in children and young people can be prevented by both selective and indicated interventions (Clarke et al., 2001; Garber et al., 2009). Selective preventive interventions identify populations that are at greater risk of developing mental disorders (such as the children of parents who have a mental illness) and target interventions on these groups. The interventions evaluated by Clarke et al. (2001) and Garber et al. (2009) selected a population at risk of developing a disorder (in both cases, children of people with a depressive disorder) and then screened these children for signs of depression (indicated intervention). While both these interventions were found to be very effective, they may be difficult to implement on a population level in some countries. For example, the identification of the initial at-risk populations (that is, children of parents with a depressive disorder) in contexts such as Australia is difficult compared to the USA. In the USA, organizations such as health-maintenance organization can be used to identify parents with depression and subsequently screen their children for signs of depression. There are no such population-level registers in Australia.
Generally indicated preventive interventions in children and adolescents have been found to be very cost-effective (Mihalopoulos et al., 2012). Similarly, Simon et al. (2012) have evaluated the cost-effectiveness of providing interventions to children who are at risk of developing anxiety disorders, finding such interventions to be very cost-effective. In a subgroup analysis, this study found that the most cost-effective strategy was to provide a parenting intervention to children whose parents also had high levels of anxiety symptoms. In fact, the parenting intervention in parents with high baseline anxiety levels became cost-saving with improved outcomes for the children when compared to a child-only intervention and a control group.
Conclusion
In conclusion, the economic costs of parental illness in terms of costs borne both by the parents themselves and by their children, who are at greater risk of developing mental illnesses or poor psychosocial outcomes, are likely to be substantial. Empirical work demonstrating the actual burden impacts, in terms of both costs and disease burden, is urgently required. Furthermore, establishment of the evidence base around the cost-effectiveness of interventions targeting parents with mental illness and their children is urgently required. It is imperative that interventions which target parents with a mental illness should include an economic evaluation alongside the main study effectiveness. Such studies should comprehensively measure the costs associated with such interventions (including broader societal impacts such as productivity effects) to both parents and their children as well as the impacts on both parents and their children. These impacts should ideally also include quality-of-life impacts that can be used in CUA to demonstrate the economic impact of such interventions in a way useful to decision-makers.
Related posts:
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E-learning professional development resources for families where a parent has a mental illness
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Parental psychiatric disorder: translating The Family Model into practice change
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