3.3  Measures of health

Several measures of health from the SoFIE data were used in order to provide a broader picture of the association between health and wealth. Health measures with a wider focus tend to be more subjective than measures with a narrower focus.

Each wave contains a single question asking respondents how they rate their health (this is referred to as self-rated health). In Wave 3 a more detailed module of health questions is asked. This includes several internationally recognised health surveys: the Short Form Health Survey (SF36) and the Kessler 10-item scale (K10) were both used. Respondents were also asked about the presence of eight chronic conditions.

Self-rated health is the most widely answered measure of wellbeing available with less than 0.1% of respondents failing to rate their own health. For the SF36 and K10 questionnaires 1.4% of respondents did not provide responses, and up to 0.7% of respondents did not provide answers to the questions on chronic conditions.

3.3.1  Short Form Health Survey (SF36)

The SF36 is a health questionnaire consisting of 36 questions that can be split into eight measures of health. Four of these measures relate to physical health and four relate to mental health. Physical and mental health summary variables are calculated from the health measures.

The SF36 has been tested and found reliable for use in New Zealand (Scott, Sarfati, Tobias and Haslett, 1999), but its validity for Māori or Pacific Island populations was later questioned as they do not view mental and physical health to be as separable as the survey assumes (Scott et al, 2000).[7]

3.3.2  Kessler 10-item scale (K10)

The K10 is a scale measuring non-specific psychological distress. It consists of 10 questions that seek to measure anxiety, depression and negative emotional states a person may have experienced in the four weeks prior to the interview. Items are rated 1 (none of the time) to 5 (all of the time). Scores for the 10 items are summed yielding a total score between 10 and 50 with lower scores signifying better health.

The K10 has been tested and validated in Australia (Andrews and Slade, 2001). It has seen wide use in Australia and around the world and has also been used in mental health surveys in New Zealand (Carter, Hayward and Richardson, 2008).

3.3.3  Choice of health survey regressors

The SF36 and K10 measures of wellbeing are referred to as the health survey measures. These surveys were analysed by the Otago School of Medicine, Wellington, who calculated measures of wellness from the raw responses.

Following the design of other analysis (Headey and Wooden, 2004), physical and mental wellbeing were treated separately. This allows for different types of ill health to have differing effects on net wealth. The physical functioning (PF) component of the SF36 survey was chosen as the preferred measure of physical discomfort. The K10 survey was chosen as the preferred measure of psychological distress.

Each of the measures of physical wellbeing collected in the SF36 survey, including the physical component summary, were considered for use in the model. The PF component was selected because of its goodness-of-fit and the nature of the questions that determine this score. The questions asked about the influence of the respondent's health on their ability to perform a range of common physical activities, including walking distances, climbing stairs, bending, lifting, bathing and dressing. The PF measure is scored from 0, representing significant problems, to 100, representing the absence of problems.

The K10 score was used as the measure of mental wellbeing, as opposed to a measure from the SF36, owing to the low correlation with the physical functioning measure and for its improvement to the goodness-of-fit of the model.[8]

3.3.4  The application of SF36 and K10 regressors

Categorical measures of physical and mental wellbeing were used as the relationship between the log of wealth and wellbeing may not be linear. Responses to each health measure were broken into three categories.

Table 3 gives the bounds on the categories, the percentage of the longitudinal population in each category and the mean and median net wealth in each category.

Source: SoFIE Waves 1-3, OSMs, longitudinal weights, supplied by Statistics New Zealand

Note: These results are not corrected for age, and younger respondents are likely to report low net wealth and better than average health status.

The breakdown of K10 scores into categories follows recommended criteria (Diener, Suh, Lucas and Smith, 1999; Phongsavan, Chey, Bauman and Brooks, 2006). This specifies four categories, breaking the high psychological distress category into high (22-29) and very high (30-50). Individuals with K10 scores greater than 30 are expected to meet the criteria for clinical intervention. The proportion of the longitudinal population with estimated K10 scores in excess of 30 (1.6%) was too small for the regression coefficient to be of use. The high and very high psychological distress categories were therefore merged.

A fourth category was also attempted with the PF score, very high physical discomfort, separating high and very high discomfort at a score of 20. Because this created a category with too few observations to provide a useful estimator the very high and high discomfort categories were merged. Only 2.5% of the longitudinal population were estimated to suffer from very high physical discomfort.

Figure 1 – Distributions of K10 and physical functioning scores

Source:  SoFIE Waves 1-3, OSMs, longitudinal weights, supplied by Statistics New Zealand

Note:

1. The three vertical lines in the body of each plot show the cut-off points between the different categories. The outermost line in each plot shows the cut-off between low and moderate discomfort/distress. The middle line in each plot shows the cut-off between moderate and high discomfort/distress. The inner most line in each plot shows the attempted (but not used) cut-off between high and very high discomfort/distress.

Figure 1 shows the distributions of the PF and K10 descriptors. The divisions between the categories for high, moderate and low discomfort and distress are included to show the approximate proportions for the longitudinal population in each category. The divisions of high discomfort and distress into high and very high have also been provided.

3.3.5  Self-rated health

Respondents were asked in each wave to rate their own health. They had five choices of response: excellent, very good, good, fair and poor. Self-rated health from Wave 2 was used; this is the only measure of health that does not come from Wave 3. This measure is used as it comes from the same wave as the wealth data and because it may include aspects of health not covered by SF36, K10 or the chronic conditions.

Self-rated health was included in the model as a categorical variable. The five possible responses for self-rated health were used as distinct categories. Merging of adjacent categories was considered, but coefficient testing revealed the different self-rated health categories were distinct from each other.

Self-rated health has the potential to be misleading as there may be no standard for responses between individuals. Two respondents with the same level of wellbeing, may rate their own health differently. Headey and Wearing (1992) suggest that people rate their own health in comparison to others of the same gender and age, to their parents and siblings and to their own recent past. If a respondent's perception is affected by non-health-related events then these may also influence their response.

Furthermore, responses may change owing to factors unrelated to ongoing health, such as catching the flu in the last month. The estimated “effect” on wealth may be biased if health is not an exogenous variable (ie, it is endogenous). A variable is endogenous if it is affected by the dependent variable or if there are unobserved variables that affect both variables. A particular type of endogeneity that is more likely to affect the self-rated health regressions than those based on more objective measures is referred to as “rationalisation bias”.[9] It is possible that an individual's wealth influences their current perspective about their health. For example, individuals with low levels of wealth, which may be linked to factors such as being unemployed, may be inclined to understate their health in order to justify their low wealth.

3.3.6  Chronic conditions

As part of the health module in Wave 3, respondents were asked whether they have ever been diagnosed by a doctor with any of the following conditions:

• asthma
• high blood pressure
• high cholesterol
• heart disease
• diabetes
• a stroke
• migraines
• depression or schizophrenia.

These will be referred to as the chronic conditions.

For each condition, other than depression or schizophrenia, the age of diagnosis was asked. This enabled the chronic conditions to be backdated so only respondents who had been diagnosed with a condition by Wave 2 were recorded as suffering from one. Individuals diagnosed with depression or schizophrenia in Wave 3 were assumed to have been suffering from the condition during Wave 2.

No indication of severity of the condition is asked, nor whether the respondent still suffers from the condition (or has suffered from it recently in the case of a stroke). As a consequence, some respondents who report having been diagnosed with a condition may no longer be affected by it. The chronic condition indicators may therefore not be an accurate indication of the presence of negative health effects owing to these conditions. Alternatively, the indicators may be advantageous as they enable the association between past ill health and current wealth to be considered.