Scale development of health-related quality of life (HRQOL) measures, including physical and mental health measures, among public datasets from Japan is needed for comparative studies on health conditions among different age, gender, and socio-economic subgroups. Multi-attributable scales of continuous/discrete variables on HRQOL could be more flexible for different kinds of epidemiologic and socio-econometric studies rather than single-item measures. The objectives of this study were to create multi-dimensional scales for physical, mental, and summary health measures and to describe the age-related trends of these scales in Japan.
We utilized data from the 2007 Comprehensive Survey of the Living Conditions of People on Health and Welfare (LCPHW: Kokumin Seikatsu Kiso Chosa) (n = 383,745) to measure physical health (0 = worst score, 16 = best score) by summarizing four items: general health status, bedridden status/mobility, self-care/usual activities, and pain (0 = worst score, 4 = best score for each item). Mental health was measured using a Japanese version of K6 (0 = worst score, 4 = best score, modified from original version in which 24 = worst score and 0 = best score). We then created a summary health scale using the simple sum of physical and mental health (0 = worst score, 20 = best score). The reliability and validity of the scales were evaluated and their age-related trends described.
The internal consistency reliability of the physical and summary health scales was not sufficiently high (Cronbach’s α = 0.64 and 0.67, respectively) and the age-related trend was smooth and monotonous. The internal consistency reliability of the mental health scale (K6) was high (Cronbach’s α = 0.90), while the age-related trend peaked at age 65–74 years.
While K6 was a measure with high reliability for describing mental health, use of the physical and summary health scale in the Japanese population requires further discussion. Additional validation tests of the summary scales also need to be performed, in which our methodology is applied to other data sets that include strict diagnostic results based on a structural interview.