Quality of life impact of mental health conditions in England: results from the adult psychiatric morbidity surveys

While the prevalence of mental health conditions is increasing in the UK, there is a dearth of country specific data on the impact of mental health conditions on health-related quality of life (HRQoL). There are a number of European studies that investigate HRQoL decrements as a result of mental health conditions but none of these includes the UK [1‐3]. The focus of many surveys has been epidemiological in nature concentrating on prevalence of mental health conditions. Health state utility values (HSUVs) take the form of an index constructed from an outcome measure normally anchored from zero to one to represent death and full health respectively. They are normally used by decision-makers to evaluate and compare cost-effectiveness of various treatments. HSUVs are particularly scarce for more complex conditions like psychosis, phobia and panic disorder [4] and disutility associated with different levels of severity.

Decision makers such as the National Institute for Health and Care Excellence (NICE) in the UK recommend that the results of economic evaluations in healthcare are presented in terms of quality adjusted life years (QALYs) which are a composite measure of health related quality of life and life expectancy. The most commonly used measures for putting the ‘Q’ into the QALY are generic preference-based measures of health, such as the EQ-5D [5] and SF-6D [6].This paper seeks to address the lack of available HSUVs associated with mental health conditions by providing estimates that can be used in economic evaluation from a representative community based sample.

Another important feature of mental health conditions is the common existence of co-morbidities, arising from other mental health conditions as well as physical ones. Understanding the impact of one condition on HSUVs cannot be done in isolation, but only after controlling for other health conditions. Previous studies of mental health conditions have largely failed to do this. There are several studies in the literature that highlight the lack of consensus on how best to calculate HSUVs [7, 8]. While attempts have been made to calculate HSUVs for co-morbid physical conditions [9], for example by adding or multiplying the effects of separate conditions, there is still overall very little empirical research on co-morbidities for those with mental health conditions.

Table 1 reports descriptive statistics for the entire sample of 11080 cases. The distribution of the SF-6D health utility index is negatively skewed with a concentration at the higher end of the scale (Additional file 1: Figure S1a). However there is no obvious ceiling or floor effect and only a small proportion of observations take the value of one (less than 5%). While the mean scores for SF-6D (0.791) and EQ-5D (0.808) are very similar, the distributions are very different. The EQ-5D distribution has a ceiling effect with 33% of observations at full health. SF-6D and EQ-5D scores range between 0.35 and 1 and between -0.38 and 1 respectively (Additional file 1: Figure S1b).

Table 2 shows the descriptive statistics on physical and mental health conditions with mean utility scores. Around 82% of people in this dataset have no mental health disorder, similar to the prevalence rate reported in the APMS reports [10, 11]. The most common problem is Mixed Anxiety Depressive disorder (MADD) found in around 10% of respondents, and this is followed by alcohol dependency, which is here defined as any level of dependency detected by the SADQ, ranging from mild to severe. The SF-6D (EQ-5D) scores for those with no physical health problem and no mental problem are 0.829 (0.851) and 0.827 (0.842) respectively. All the scores for the EQ-5D are marginally higher than the SF-6D. Long-term depression and depression sufferers have the lowest SF-6D (EQ-5D) scores of 0.532 (0.513) and 0.551 (0.537) respectively. According to the neurotic symptoms score, those with more severe symptoms have monotonically lower utility scores as measured by both SF-6D and EQ-5D.

The results from the OLS and tobit regressions for SF-6D and EQ-5D respectively are shown in Table 3. Marginal effects have been calculated at the means of the explanatory variables for tobit regressions. The highest decrements in HSUVs are observed for depression, MADD, long-term depression and GAD for both SF-6D and EQ-5D. The HSUV decrements in SF-6D (EQ-5D) scores are as follow: depression -0.137 (-0.159); MADD -0.136 (-0.127); long-term depression -0.100 (-0.118); GAD -0.086 (-0.070); phobia -0.083 (-0.081); panic -0.077 (-0.076); OCD -0.057 (-0.034) and; drug dependency -0.027 (-0.011). The three mental health conditions that do not have a statistically significant effect on health utility are psychosis, personality disorder and alcohol dependence. The coefficients for psychosis and personality disorder may be insignificant due to the small numbers of people (81 and 31 respectively) suffering from these conditions. All of the physical health conditions, except eye complaints, are statistically significant at 1% level of significance. In the models for both SF-6D and EQ-5D, socio-demographic characteristics were controlled for. They are not reported in this paper but are available by request from authors.

For the OLS regression for SF-6D, the adjusted R² is 0.41 but the Ramsey Regression Equation Specification Error Test (RESET) [21] and Pregibon [22] link test statistics suggest that the model suffers from omitted variables and misspecification problems respectively. It is worth noting that these tests also revealed misspecification problems with both tobit [23] and Postestimation Generalized Linear Models (PGLM) models [24, 25] which were also explored for the SF-6D (not reported here). The marginal effects of physical and mental health conditions on the SF-6D index are very similar regardless of the estimation procedure and functional form chosen, a finding similar to that of Jones [26] who compared a number of different models for modelling health care cost data. In the case of EQ-5D as the dependent variable, the link test suggests that the model is correctly specified making tobit regression a better model than OLS in this case.

Our estimates from over 10,000 responses to the APMS 2000 and 2007 show that all but three of the mental health conditions considered here have a statistically significant and relatively large adverse effect on health utility measured by both SF-6D and EQ-5D indices. These effects are larger than for self-reported physical health conditions. Another study also found that HRQoL decrements from mental health conditions are higher than those reported by suffers of chronic physical conditions [1]. Depression, MADD and GAD are the mental health conditions that impact most on health. The three mental health conditions that are not statistically significant are alcohol dependency, personality disorder and psychosis. In considering co-morbidities, we have used interaction terms and in most cases they are significant confirming the fact that we cannot simply add HSUVs decrement of co-morbid health conditions. The impact on HSUVs is generally less than the addition of the separate health conditions. When considering interactions between mental and physical health conditions, the majority are positive, however in one case the interaction is negative and significant suggesting that the presence of both conditions reduces health utility by a larger amount that the additive model would suggest. Brief exploration of the effects of the severity of anxiety and depression on the health utility index, suggest that for depression the results are as expected, the utility decrement increasing with the severity of the condition. However, for anxiety, panic and phobia, only two severity levels were considered.

Utility decrements have been measured using both SF-6D and EQ-5D predicted using a published mapping function. Both measures on the whole depict a similar picture. In general, for most physical and mental health conditions, the decrements shown by SF-6D and EQ-5D are similar and this finding is supported by a study comparing SF-6D and EQ-5D across seven patient groups with physical health conditions [27]. A multi-centre randomised control trial in Netherlands [28] found that EQ-5D utilities led to significant higher health gains even though EQ-5D mean scores were lower than SF-6D scores at baseline. A study on patients with schizophrenia revealed similar EQ-5D and SF-6D mean scores at baseline but higher EQ-5D mean scores higher at follow-up [29].

In model [3], the utility decrements for eye complaints are statistically insignificant. This finding is line with a recent review where it was found that SF-6D and EQ-5D are inadequate measures for such conditions [30]. Similarly, the insignificant coefficient for psychosis is in line with the finding that generic measures are not suitable for use in schizophrenia, a psychotic disorder [31]. An additional reason why personality disorder and psychosis are not statistically significant could be due to relatively small numbers, as less than 1% of the sample has these conditions. For alcohol dependency we explored whether the insignificant result was due to our relatively broad definition of any dependency, by testing the effects of moderate and severe dependence on alcohol (as defined by the SADQ) alone, but these were not statistically significant either. Another study found that alcohol dependence was insignificant but similar to this paper, it was not possible to understand how and why this was the case [1].

The effects of co-morbidities are investigated through interaction terms. For the majority of mental health co-morbidities, the interaction terms are positive and often offset the decrement caused by a single condition. While it is understandable that this may be the case, clinically it is difficult to explain. It may be that patients get better at managing their conditions having been exposed to another mental health condition. In the presence of a mental health condition and a physical health condition, some of the results are very surprising. First, several of the interaction terms as measured by EQ-5D score are statistically insignificant even though the both main effects are statistically significant. These include GAD and muscular/skeletal complaints, GAD and digestive complaint, GAD and urinary complaints, MADD and urinary complaints, MADD and skin complaints, depression and respiratory complaints, phobia and muscular/skeletal complaints. This is not the case when the dependent variable is SF-6D. It may be due to the artefact of the mapping approach. In some cases, the main effects are not statistically significant but the interaction terms are. Examples include alcohol dependency and digestive complaints, where alcohol dependency is insignificant but the interaction term is significant. In this case, it is not clear whether it is acceptable to use the HSUVs thus generated. A possible explanation for the latter result is the fact that alcohol may be used to alleviate the symptoms of GAD and MADD.

Despite these limitations, this study has a number of interesting findings. First, the estimates provided are based on a representative sample of the UK population. Generally HSUV are generated from trial data and there are always questions as to whether they can be generalised and used outside the trial setting. Therefore the decrements associated with the various mental health conditions and co-morbidities in this paper can be readily used in decision-analytic models in assessing cost-effectiveness of health technologies. Second the APMS is regarded as the most reliable available data in the UK in the area of mental health.

There are several implications of this paper. First, background characteristics need to be controlled for when estimating HSUVs. This is very important but often not accounted in single condition studies. As a result the HSUV decrement associated with a particular health condition is possibly higher which, in turn has implications in cost-effectiveness analysis. Second, the paper has also been able to generate estimates for HSUVs associated with long-term depression. Third, the paper shows that simply adding HSUVs for co-morbid health conditions is not appropriate. Fourth, it provides decrements that can be used to reflect severity of the following conditions: depression, anxiety, panic and phobia. These are useful parameters that can be used in decision analytic models where conditions of patients may be shown to progress.