Commentary on Jorm (2018) – Australia’s ‘Better Access’ scheme: Has it had an impact on population mental health?

Jorm (2018) answers the ‘big’ question he posed seven years earlier in his commentary regarding the first evaluation of the Better Access initiative undertaken by Pirkis et al. (2011). He stated that the real test of the initiative’s effectiveness would be whether population health indices of mental health show improvement over time (Jorm, 2011). The paper by Jorm (2018) is simplistic in its approach and concludes that Better Access has not had any measurable impact on population mental health. While this appears to be supported in the three charts present in the paper, I believe that this conclusion may be premature for four key reasons.

First, the issue of confounding cannot be excluded from the conclusions drawn by Jorm. While he states that the rapidity of the uptake of Better Access helps to mitigate the impact of confounding, I am not convinced that this is necessarily true. For example, one major societal change that has occurred over the same 10-year period has been the population saturation of social media in particular (for example, see www.pewinternet.org/2018/03/01/social-media-use-in-2018/) and Internet use (ABS, 2018). While the psychological impacts of such rapid increases are currently unknown, this is an example of a population level change that has occurred during the time period discussed by Jorm and could theoretically act as a confounder.

Second, an important consideration in determining value-for-money is what ‘costs’ are included. Jorm highlights costs incurred by the Federal Government (via the Medicare Benefits Schedule (MBS)) and out of pocket costs associated with MBS items. While access to MBS items has now improved (and hence increased some costs), perhaps costs in other aspects of the economy – such as hospitalisation, carer or even specialised mental health treatment costs – have decreased. Even though outcomes, such as the K10, may be unchanged it is not inconceivable that costs in other sectors may have reduced. The National Health Survey (NHS) also contains data on the healthcare services used by individuals. Therefore, rather than relying on unlinked disparate datasets, a better approach may have been to include an analysis of actual health service usage in the NHS and its potential relationship to K10 scores over time.

Third, as a health economist, a question never far from my mind is whether any initiative or intervention represents good value-for-money. Within an economic framework, the issue of opportunity cost is integral, that is, what would be the value of an alternative use of those resources. Inherent to this consideration is the term ‘value’. Jorm (2018) highlights two sources of ‘value’ – improvements in K10 scores and reductions in the suicide rate. However, from the figures presented in the paper, it is not clear that there have not been any changes observed in the K10 scores over time. It is true that the proportion of people with very high K10 scores does not seem to have shifted, although this is not true of the proportions of people with high scores. Figure 2 shows a dip in the proportion of people with high and very high scores after the introduction of Better Access, although there was a downwards trend that had started before that. Perhaps the Better Access initiative is more effective for people with mild-moderate scores/symptoms of illness rather than people at the more severe end.

Finally, while both the K10 and suicide rates are important outcome measures, they are not the only outcomes upon which ‘value’ can be judged. Within other sectors of the health economy, for example, pharmaceuticals more generally, quality adjusted life years (QALYs) are an acceptable outcome of healthcare and thus ‘value’ of health spending (https://pbac.pbs.gov.au/section-3a/3a-5-health-outcomes.html). QALYs are a composite measure of life years adjusted for the ‘quality’ of those life years. While there are different methods of measuring the quality in a QALY, the most common is via the use of multi-attribute utility instruments (MAUIs). MAUIs are essentially health-related quality-of-life questionnaires with an added utility scoring algorithm which weighs the relative importance of the various domains of quality of life included within the questionnaire. In some of our earlier work (Mihalopoulos et al., 2014), we found that the K10 in particular is well correlated to the main MAUIs used to determine QALYs. Although this is true for people with self-rated depression who completed the survey, it may not be true for people with anxiety disorders. Therefore, while it is likely that changes in MAUIs are likely to mirror what has been demonstrated in the K10 results, at least for people with depression, the relationships are not perfect.

Other important outcomes, such as engagement in economic activity, usually defined as productivity impacts within economic evaluations, are not well captured by many quality-of-life or clinical/disorder measures. For example, the recently announced Productivity Commission’s enquiry into the mental health sector (http://jaf.ministers.treasury.gov.au/media-release/024-2018/) has stated that the focus of the enquiry will be on individuals’ social and economic participation. As the detailed terms of reference of the enquiry have not yet been publicly released at the time of writing this commentary, it is unknown to what extent other outcomes will be considered or how social and economic participation will be defined. In any case, it is hoped that the fundamental question of what we, as a society, would like to accept as an appropriate definition of ‘value’, not only from our mental healthcare spending but our health and societal spending more generally, is raised and given the attention it so desperately deserves.