Aims of the study

This paper has three objectives: (i) to estimate the impact of mental health disorders on quality of life in England (ii) to compare and contrast the impact of physical health and mental health co-morbidities on quality of life (iii) to assess the impact of severity of depression, anxiety, phobia and panic disorders on quality of life.

Specifically we estimate the decrement associated with various mental health disorders using the SF-6D and EQ-5D indices from a representative sample of the UK general population and controlling for a wide range of background variables.

Data

We used health-related quality of life data and information on health status collected in the Adult Psychiatric Morbidity Survey (APMS) carried out in 2000 and 2007 [10, 11]. This is a rigorously conducted general population survey aiming to provide information on the prevalence of psychiatric conditions among people living in Great Britain, as well as their associated social disabilities and use of services. Each APMS recruited about 8,000 working age adults in private households. APMS is unique in the UK for having data on a broad range of conditions including common mental health disorders like depression, anxiety and obsessive compulsive disorder, psychotic conditions, personality disorders and alcohol and drug dependence. They also contain general health measures including the SF-12 health index, a measure from which the SF-6D preference based utility index can be obtained [12]. There is also information on socio-demographic data, education and employment, income and debt, accommodation and stressful life events. While the 2000 survey covered England, Wales and Scotland, APMS 2007 only interviewed people in England so our analysis uses data for England only.

In both years APMS interviews are conducted in two stages. First, a computer assisted personal interview in the respondents own home covering neurotic symptoms and disorders using the Clinical Interview Schedule Revised (CIS-R) and screening items on personality disorder and psychosis. CIS-R is a structured interview that has been standardised so that it can be administered by social survey interviewers. It enquires about 14 common neurotic symptoms allowing categorisation according to ICD-10 criteria. CIS-R also measures the severity of the condition and is widely used [13, 14]. A second stage sample was chosen comprising respondents who satisfied screening criteria for psychotic and personality disorder. The second stage interviews were conducted by trained psychologists using Schedules for Clinical Assessment in Neuropsychiatry (SCAN) and Structured Clinical Interview (SCID-II). In 2000 (2007) there were 8580 (7461) initial interviews, a response rate of 54 (57)%. At the second stage there were 638 (630) interviews and a response rate of 73 (74)%. Our analysis sample comprises 5688 individuals in 2000 and 5388 individuals in 2007. Data for 2000 and 2007 are pooled.

Measuring HSUVs

Two generic preference-based measures were derived from the SF-12 data: SF-6D and the EQ-5D. The SF-6D health utility index is derived from individual responses to the SF-12, a generic health measure based on items taken from the SF-36 health survey, a standardised questionnaire used to assess patient health [15]. Brazier and Roberts [9] developed a preference based index for the SF-12 using an algorithm estimated from standard gamble valuations of a sample of SF-6D states obtained from members of the UK general population [6]. The index takes values that range from zero (equivalent to dead) to one (full health).

The EQ-5D questionnaire consists of 5 simple questions on mobility, self-care, usual activities, pain and discomfort, anxiety and depression. As raw data was not collected from respondents using the EQ-5D questionnaire, EQ-5D scores have been generated through mapping from SF12 items using a response approach mapping [16]. It is deemed important to present the utilities using the EQ-5D, since the former is the method preferred by NICE in the reference case [17]. In the response mapping approach, multinomial logistic regression is used to estimate the probability that a respondent will select a particular level of response to questions in the EQ-5D, using individual question responses from the SF-12 as predictors. EQ-5D responses are predicted using Monte Carlo simulation methods and UK tariffs are then applied to the raw scores to generate the EQ-5D index [5].

Measuring severity of conditions

It is often useful to know the severity of the condition, as quality of life decrements and health care costs are usually much higher for greater levels of morbidity. Each condition is diagnosed via a set of four questions from the CIS-R. For example in the case of anxiety these are: (i) felt generally anxious/nervous/tense for 4 days or more in the past seven days; (ii) in past seven days anxiety/nervousness/tension has been very unpleasant; (iii) in the past seven days have felt any of the following symptoms when anxious/nervous/tense (Racing heart, sweating or shaking hands, feeling dizzy, difficulty getting one’s breath, dry mouth, butterflies in stomach, nausea or wanting to vomit); (iv) felt anxious/nervous tense for more than three hours in total on any one of the past seven days. Each question scores one if that symptom was present, giving a total anxiety score ranging from zero for no symptoms to four. This score can be used as a measure of severity of the condition. A similar approach was taken with depression, panic and phobia.

Analysis

Where U _i is health utility for respondent i; M is mental health, P is physical health and X is a set of background characteristics, ϵ_i is a random error term.

Two separate sets of analyses are carried out with SF-6D and EQ-5D as dependent variables. The initial modelling was undertaken using Ordinary Least Squares (OLS) but this can be criticised. OLS estimates are biased and inconsistent, because both the SF-6D and EQ-5D distributions are skewed and in addition the EQ-5D distribution is truncated with many observations at the upper value of one [18]. To overcome these problems, OLS models for SF-6D are estimated with robust error variance as Breusch-Pagan/Cook-Weisberg tests results suggest heteroskedastic errors, and for the EQ-5D, tobit models are used to deal with the truncated nature of the data.

Mental health (M) is measured by a set of dichotomous variables to represent the presence of specific disorders. Diagnosis of specific disorders were assigned by the Office of National Statistics using answers to various sections of the CIS-R and applying algorithms based on the ICD-10 diagnostic criteria; these disorders are: generalised anxiety disorder (GAD), mixed anxiety depressive disorder (MADD), panic disorder, obsessive compulsive disorder (OCD), phobia, and depression. Psychosis and personality disorder are assigned via the Stage 1 screening questions, alcohol dependence is defined according to the Severity of Alcohol Dependence questionnaire (SAD-Q) and drug dependence defined according to questions used in the US Epidemiologic Catchment Area survey. A variable was generated to represent people who self-report that they have long-term depression lasting for a period of 2 years or over [19]. More detail on definitions for each disorder can be found in the APMS technical reports [10, 11].

Physical health (P) is measured by a set of dichotomous variables denoting the presence of self-reported long-standing health conditions: muscular-skeletal, respiratory, digestive, heart and circulatory, urinary, skin, ear, eye, neoplasm, blood disorder, and infection. The set of background variables (X) include age, marital status, presence of children aged 16 or under in the household, employment status, ethnicity, education and income. Dummy variables for regions and year are also included. A full list of variables and definitions can be obtained from the corresponding author.

Where IM is a set of dichotomous variables representing first order interactions between the seven mental health conditions described above and IP is a set of dichotomous variables representing first order interactions between the seven mental health conditions and the seven physical health conditions described above. It was not possible to investigate co-morbidities for personality disorder, psychosis and panic due to the small number of observations. In addition some physical conditions could not be considered due to small sample sizes, including ear complaints, neoplasm, blood disorder and infectious disorder. Data from the APMS was downloaded from the UK Data Archive [20] and STATA 11 was used for the analysis.

Co-morbid Health Conditions

Severity of mental health condition

For anxiety, while there is a gradient overall from level 1 to 4, the estimated coefficient for levels 3 and 4 have very similar decrements. In this case, the levels have been aggregated to represent only two severity levels with the more severe level showing a higher SF-6D (EQ-5D) decrement of -0.070 (-0.068) compared with the lower severe level of -0.047 (-0.032).

The severity levels for phobia and panic have also been aggregated to distinguish between two severity levels. In the case of phobia, higher decrements are observed in both SF-6D and EQ-5D scores for the more severe levels. It is noted that the EQ-5D scores are not significant. However, for panic disorder both SF-6D and EQ-5D show a lower decrement for the more severe level and only the SF6D value is significant. This is possibly mainly due the fact that there are only 57 and 64 individuals in the less severe and more severe categories respectively.

Limitations

Overall the models we have estimated have good explanatory power but the RESET test does suggest misspecification problems. We have explored both tobit and generalised linear models for the SF-6D but these were still wrongly specified. The EQ-5D index also suffers from ceiling effects and therefore more sophisticated models may be used to address that. Despite the weaknesses of OLS regressions in the case of SF-6D, it was decided to present the results as they are easy to interpret and in the absence of better estimates, those generated in this paper can be readily used in decision-analytic models.

It is worth stressing here that the data we have used to estimate these models is not based on clinical diagnosis of mental health conditions, but instead on well-established instruments administered by trained interviewers. Furthermore, while data on the mental health conditions are reliable, the data on physical conditions are self-reported and are therefore less reliable since there was no confirmation of the conditions. Finally, the data is limited to those living in private households, though this accounts for the vast majority of people with these conditions.

There may also be a concern as to whether a generic measure of health like the SF-12 adequately assesses the impact of all mental disorders – particularly personality disorders and psychosis. The other limitation is that the EQ-5D estimates are based on a mapping function that been shown to have a tendency to underestimate values at the upper end of the scale and overestimate values at the lower end. Other mapping functions have not overcome this limitation and it may result in an underestimate of the impact of different conditions [32].