4.2 Health measures in HWR and NZLSA

Health and health-promoting behaviours are a key focus in the HWR/NZLSA dataset, and are measured via self-rated indicators across all three waves of data collection. The assessment of both positive and negative health behaviours (eg, exercise intensity/duration, smoking, and alcohol consumption) and the existence of chronic health conditions (eg, heart trouble, diabetes mellitus, visual impairment) provide a strong evidence base for tracking the development of chronic ill health and detrimental health trends.

This section will describe the measures of health status, and present some descriptive statistics of the bivariate relationship between health and participation.

4.2.1 Health scales and self-rated health

To measure specific levels of perceived health, HWR/NZLSA uses an internationally standardised, multi-faceted health measure: the Australian and New Zealand adaptation of the SF36 Health Survey Version 2 (SF36v2: Waves 1 and 2) and an abbreviated version called the SF12v2 (Wave 3). The SF36v2 (Ware, et al., 2000) is a 36-item measure of health, which focuses on eight physical and mental health sub-scales, and one indicator of general health change.

In order to ensure direct compatibility of health scores across all three waves, the SF36v2 for waves 1 and 2 have been rescored into their respective SF12v2 versions.

The SF12v2 is a 12-item abbreviated version of the larger SF36v2, which still targets eight domains of physical and mental health. A summary of the health domains considered in SF12v2 is given in Table 3. As with the larger SF36v2, the SF12v2 scores for each domain are standardised with a mean of 50, a range of 0-100, and weighted such that they may be interpreted in the same direction: higher scores indicate better health.[3]

The SF12v2 sub-scales for each of the domains in Table 3 have been combined to provide two summary scores for physical and mental health status: the Physical Component Summary (PCS) and the Mental Component Summary (MCS). The PCS and MCS are computed following a three-step standardised procedure.

First, the scores from all eight domains are standardised using a linear z-score transformation. Z-scores are calculated by subtracting the domain means for the general population from each individual’s domain score, and then dividing by the corresponding US population standard deviation.

Second, the z-scores are multiplied by the domain factor score coefficients for PCS and MCS and summed over all eight domains. The final score is calculated by standardising the PCS and MCS such that they have a mean of 50 and a standard deviation of 10.

Scott (2000) showed that the two-dimensional structure of the original SF36v2 did not clearly differentiate between the components of physical and mental health for older Māori. However, there have been no explorations of the degree to which this structure (as measured by the SF12v2) differentiates between mental and physical health for Māori or non-Māori.

The self-rated health measure is a very general measure of health. Specific indicators of health such as chronic condition indicators are likely to be imperfectly correlated with work capacity in many cases (Bound, 1991). The SF12v2 overcomes these issues, in that it summarises various aspects of health relevant for the participation decision, particularly health related functional limitation, the extent to which health affects daily activities and bodily pain, in addition to perception of general health (Ware, et al., 2002).

There is increasing recognition of the value in using the SF12v2, rather than its more established SF36v2 parent measure, in population-based research. First, the SF12 survey versions are considerably smaller than their SF36 parent measures, which reduce participant burden and item redundancy, while increasing participant response likelihood (Han, et al., 2002; Ion, et al., 2011).

Second, there is little explanatory power lost in choosing the SF12v2 over the SF36v2. The composite and sub-scale scores from the SF12v2 show excellent compatibility with those derived from the SF36v2 across a variety of populations (see Lee, et al., 2008; Ware, et al., 2002) and also show very good internal reliability, and excellent convergent validity with existing self-rated measures of physical and mental health (Cheak-Zamora, et al., 2009).

Third, although potentially open to self-report bias, research consistently shows that the SF12 physical and mental health scores reliably predict the likelihood of condition onset, hospitalisation and mortality across different populations (Arnold, et al., 2009; Dorr, et al., 2006; Haring, et al., 2011).

Finally, opportunities for cross-national comparative analyses are now increasing as considerable effort is being invested in standardising it for use in non-English speaking countries, including China (Lam, et al., 2010), Iran (Montazeri, et al., 2011; Rohani, et al., 2010), and Israel (Bentur and King, 2010). Thus, the brevity of the SF12v2, the focus on the same health domains as the SF36v2, and its increasing international applications explain why it is rapidly becoming the measure of choice for large population-based health surveys (Cheak-Zamora, et al., 2009; DiBonaventura, et al., 2011).

Table 4 shows the distribution of the SF12v2 physical health summary measure over age groups.

Table 4 – Distribution of SF12v2 physical health summary measure

Source: HWR and NZLSA longitudinal sample

Note: Boxes consist of the median and inter-quartile range. The whiskers terminate at the adjacent values.

Table 5 summarises the SF12 health scores for those in the work force and those retired, by age group and gender. It appears that there is little difference in the mental health scores between the working and the retired. However, those retired (both male and female) have lower physical health scores than their counterparts in participation. A five-unit change in the SF12 scores indicates a clinically-significant change in health status.

Source: HWR and NZLSA longitudinal sample

On average, those who are retired have poorer health status. However, poor health is not a barrier to participation for all. In fact, a large proportion of individuals who indicate they are in fair or poor health remain in the workforce. This is reflected in Figure 4, which indicates the proportion of individuals participating by age group and self-rated health status. While the likelihood of participation decreases with age and health, there remain many individuals in fair or poor health in the labour force.

Figure 4 - Proportion participating by age group and health status

Source: HWR and NZLSA longitudinal sample